Harnessing io t-global-development

Harnessing
the Internet of Things
for Global Development
A CONTRIBUTION TO THE UN BROADBAND COMMISSION
FOR SUSTAINABLE DEVELOPMENT

Harnessing the Internet of Things for Global Development
Table of Contents
Forewords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Mr. Houlin Zhao (ITU Secretary-General). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Mr. Chuck Robbins (CEO, Cisco Systems). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Extending our Hyperconnected World through the IoT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Defining the Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
How the Internet of Things is Emerging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Functionalities of the Internet of Things. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Functionality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Connectivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Applications across Different Sectors in Development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
The Internet of Things in a Developing Country Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Water and Sanitation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Agriculture and Livelihoods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Resiliency, Climate Change and Pollution Mitigation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Natural Resource Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Energy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Other Sectors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Challenges to the Deployment, Impact and Scale of the IoT in Developing Countries . . . . . . . . . . . . . . . . . . . . . . 41
Challenges of the Internet of Things. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Technical Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Policy Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Overlapping Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Recommendations – Supporting the IoT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Annexes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Annex 1: IoT Projects by Sector. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Annex 2: The different characteristics of wireless IoT connectivity options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Annex 3: Sample sensor prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
1

Figures
Figure 1: Miniaturizing & Multiplying – Getting Smaller & More Numerous
(ITU, Mary Meeker, the Brookings Project). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 2: The Intersection between IoT and Big Data (Cisco). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Figure 3: From Individuals to Society – Examples of data generated by the IoT (ITU). . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 4: Different IoT Applications with Different Characteristics (ITU). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Figure 5: Range of Common Sensors (Harbor Insights). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Figure 6: Sensors by Retail Cost (Cisco). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Figure 7: Comparing IoT Connectivity Technologies (Cisco). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Figure 8: Access to Energy, Water & GSM Population Coverage in Sub-Saharan Africa (GSMA). . . . . . . . . . . . . . . 26
Figure 9: The Virtuous Circle of Development Impact (Cisco). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Figure 10: Areas of Highest Potential Impact across Different Sectors (ITU). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Figure 11: Monitoring the Movements of People during the Ebola Outbreak. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Figure 12: Summary of Emerging Challenges in relation to the IoT (Cisco). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Table 1: Functionality within IoT Technologies (ITU). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Table 2: Sensor Types, Functionality, and Examples (Cisco). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Table 3: Examples of IoT interventions Mapped to the MDGs and SDGs (Cisco) . . . . . . . . . . . . . . . . . . . . . . . 39-40
Tables
2

Acknowledgements
This report has been written by Phillippa Biggs (ITU), John Garrity (Cisco), Connie LaSalle (Cisco) and
Anna Polomska (ITU), under the supervision of Dr. Robert Pepper (Cisco) as a contribution to the ITU/
UNESCO Broadband Commission for Sustainable Development.
Mitch Hulse and Mary Carol Madigan provided research support, with additional review provided
by Dr. Ruba Borno (Cisco).
The authors wish to thank the following people and organizations who generously contributed their
time and insights through interviews and discussion to this report (listed alphabetically by organization/
project, and individual’s name):
●● American Red Cross (Abi Weaver)
●● DAI (Robert Ryan-Silva)
●● Echo Mobile (Zoe Cohen, Jeremy Gordon)
●● Integra (John Waugh, Eric White)
●● International Center for Theoretical Physics (Marco Zennaro)
●● Kopernik.info (Edwin Mulianto)
●● Network Startup Resource Center (Jonathan Brewer)
●● Nexleaf Analytics (Martin Lukac, Nithya Ramanathan, Shahrzad Yavari)
●● Oxford University - SmartPump Mobile Enabled Transmitter (Patrick Thomson)
●● Portland State University - SWEETSense, Inc. (Dexter Gauntlett)
●● Rainforest Connection (Dave Grenell)
●● United States Forest Service (Dr. Lindsey Rustad)
●● University of California Center for Effective Global Action (Temina Madon)
●● University of Washington - Smart Connect (Richard Anderson)
●● Wadi Drone project, NYU Abu Dhabi (Matt Karau, Martin Slosarik)
●● WellDone – MoMo (Austin McGee, Tim Burke)
●● World Agroforestry Center (Siddharth Mohan)
●● World Bank (Edward Anderson)
The views contained in this report may not necessarily reflect the views of the ITU or Cisco Systems.
*Front cover image: Achieving the Sustainable Development Goals (SDGs) with the Internet of Things
3

Foreword by Mr. Houlin Zhao, ITU Secretary-General
After more than a decade of discussion and anticipation, the Internet of Things is now firmly on its way. This is a major
development, which promises to extend our online world in a myriad of ways. Connecting things, as well as people,
offers prospects of new ways of monitoring situations, learning and responding in real-time.
In some sense, the “Internet of Things” is a misnomer. The Internet of Things is not a single, unified network of
connected devices, but rather a set of different technologies which can be put to work in coordination together at the
service and to the ultimate benefit of people in both developed and developing economies. This set of Internet of Things
technologies is realizing a vision of a miniaturized, embedded, automated environment of devices communicating
constantly and automatically. However, connecting up devices or robots (whether they are bridges, fridges or widgets)
is only a means to an end — the really interesting part arises in terms of what can be done with the data obtained, and
the learning outcomes for improving our future.
While researching this report, my staff learned it is not possible to focus solely on the technologies, at the risk of
ignoring the human context in which these technologies must work. There are many difficult trade-offs involved — only
some of which are technological (for example, the trade-off between robustness and reliability, and the sophisticated
functionality of sensors on a water pump). Other trade-offs enter into broader issues (for example, gaps between
technical security and users’ perceptions of security and trust, or the detailed information yielded by geo-localization
technologies). Moreover, the purpose for which technology and applications are developed does not always end up as
the sole — or even major — purpose for which they are actually used.
This report raises many important questions. Nevertheless, asking the right questions is also an important part of any
learning process, and I welcome this report’s thoughtful exploration of the use and applications of some of these
different technologies in the context of developing countries.
Houlin Zhao
ITU Secretary-General
4

Foreword by Mr. Chuck Robbins, CEO, Cisco Systems
At Cisco, we believe that technology has the capability to transform lives. Over the past few years, we have seen
companies, cities and countries move to digitize all that they do, driving positive results in numerous ways. Yet
across the world, many people still must confront a number of difficulties from climate change, endemic poverty, and
environmental degradation, to the lack of access to quality of education, and communicable disease. While these
big issues might seem insurmountable, we believe that technology can play a critical role addressing many of these
challenges, while creating a host of opportunities.
Today, the Internet of Things is improving the day-to-day lives of citizens around the world. In cities from Barcelona
to Songdo to Rio de Janeiro, Internet Protocol (IP)-connected sensors are monitoring traffic patterns, providing city
managers with key data on how to improve operations and communicate transportation options. Similar information
flows are improving hospitals and healthcare systems, education delivery, and basic government services such as
safety, fire, and utilities. Sensors and actuators in manufacturing plants, mining operations, and oil fields are also helping
to raise production, lower costs, and increase safety.
In both developed and developing countries, the Internet of Things is also helping monitor critical vaccines through the
use of IP-connected thermometers. Moisture sensors in agricultural fields now alert farmers to the exact needs of food
crops, and acoustic sensors in protected rainforests are helping to curb illegal logging.
For the IoT to have an even greater impact, there is still more we can do to improve the deployment of these
technologies in developing countries. Network deployment, power requirements, reliability and durability are all uneven,
at best, and policy considerations concerning access to data, legacy regulatory models, standards, interoperability,
security, and privacy need to be addressed.
We are in the early stages of IoT adoption and are pleased to see the great impact that the IoT is already making for
people, companies, industries and countries around the world. This report aims to serve as a guide for how the IoT
can be fully utilized to improve the lives of people everywhere. The application of the IoT to solve many of the world’s
challenges is limited only by our imagination.
Chuck Robbins
CEO, Cisco Systems
5

Executive Summary
6

This report explores the current use and potential of
Internet of Things (IoT) technologies in tackling global
development challenges, highlighting a number of
specific instances where IoT interventions are helping
to solve some of the world’s most pressing issues. It
presents summary conclusions on what is required for
the IoT to reach billions of people living in the developing
world, and also to accelerate income growth and social
development as a result.
Information and communication technologies (ICTs)
such as mobile phones, Internet use and Big Data1
analytics are pervasively utilized in global development
projects (in a field often known as ICT for Development,
or ICT4D) to improve outcomes and deliver services.
Recently, this field has experienced strong growth. For
example, in a recent presentation, Carolyn Woo, CEO
of Catholic Relief Services (CRS), identified 157 new
development assistance projects, each started by CRS
between 2014 and 2015, that incorporate ICTs (primarily
mobile telephony2
). Similarly, at Johns Hopkins University
alone, as of May 2015, there were over 140 mHealth
(mobile phone-enabled healthcare) projects across
the developing world.3
And an April 2015 review at the
World Bank identified at least thirty-two projects that
specifically incorporate Big Data analytics.4
As evidenced
by these examples, mobiles are highly integrated in
development projects already. Connected sensors and
M2M connectivity represent the next frontier in the
ICT4D story.
After having been coined as a term in 1999 by Kevin
Ashton,5
and after more than a decade of discussion and
anticipation, the Internet of Things is finally emerging.
This is a major development, which promises to change
our way of doing things through better information in
1 “Big Data” refers broadly to a data set, comprised of structured or
unstructured data, so large or complex that traditional data processing
applications are not sufficient for analysis.
2 Keynote presentation by Dr. Carolyn Woo of Catholic Relief Services http://
schd.ws/hosted_files/crsict4dconference2015a/84/2015%20ICT4D%20Confer-
ence%20Welcome%20Presentation%20Final.pdf
3 Keynote presentation by Dr. Alain Labrique of Johns Hopkins University at the
Catholic Relief Services 2015 ICT4Development Conference, Chicago, May 27,
2015.
4 “Big Data for a More Resilient Future”, World Bank Group/ 2015 Spring Meet-
ings, Big Data for Development, Summary of Projects.
5 Kevin Ashton, “That ‘Internet of Things’ Thing, in the real world things matter
more than ideas,” RFID Journal, June 22, 1999.
real-time and improved learning opportunities. IoT is
closely related to the concepts of Machine-to-Machine
(M2M) communications and Wireless Sensor Networks
(WSN) on the connectivity side, and to Big Data in
terms of the content outcomes produced. The IoT also
comprises the data produced and transmitted between
machines (M2M), as well as between machines and
people (M2P). Key elements include machine-produced
data (e.g., from sensors), and the communication of that
data (via connectivity technologies).
Many different stakeholders are involved in active IoT
projects on the ground, including industry members,
universities, NGOs, and tech start-ups, each contributing
different strengths. The IoT is not just a story for
industrialized economies or industrial applications, but
is equally relevant for developing countries. The IoT
and connected sensors are driving improvements to
human wellbeing in healthcare, water, agriculture, natural
resource management, resiliency to climate change and
energy (as reflected in the UN’s post-2015 sustainable
development agenda). The research for this report
uncovered many interesting examples and applications
of the IoT in developed economies. However, these
are beyond the scope of this report, which focuses on
impactful applications of the IoT for developing countries.
When determining which IoT application fits best
for a particular context, there are many trade-offs
and compromises involved. Technical trade-offs
include different characteristics among connectivity
technologies, including, but not limited to: performance,
efficiency, reliability, robustness, flexibility, range,
power requirements, data throughput, cost (of sensors,
connectivity modules and service) and licensed
versus unlicensed spectrum. For large-scale systems
including hundreds of thousands of sensors, devices
and/or readers, high reliability levels are likely to prove
important. Cultural context on the ground also matters,
and it should be taken into account, along with technical
considerations.
Huge new opportunities are now opening up through
improved access to and use of Big Data techniques,
which offer learning opportunities to improve real-world
processes and enhance decision-making over the
7

short-, medium- and long-term in healthcare, education,
emergency services and disaster response, among a
variety of other application areas.
Impactful IoT interventions in development can improve
efficiency (achieving similar levels of impact with fewer
resources) and/or enhance effectiveness (increasing
impact with similar levels of existing resources). In
advancing global development, IoT interventions are
helping to improve research, public policy, basic service
delivery and the monitoring and evaluation of
programmes across a range of different sectors. This
report discusses examples of use cases of the IoT in
healthcare, water, agriculture, natural resource
management, resiliency to climate change and energy.
The IoT has regulatory implications across the areas of
licensing, spectrum management, standards,
competition, security and privacy – only some of which
are the familiar territory of telecom regulators, compared
to other domains where non-telecom regulators may
typically take the lead.
Photo credit: Marco Zennaro. GSM connectivity node in Nairobi.
Maximizing the benefits of the IoT is likely to require more
coordinated regulation across all sectors, with telecom/
ICT regulators working closely with their counterparts
in data protection and competition, but also with
emergency services, health and highway authorities.6
Laws and regulations on data will need to be
reconsidered carefully in view of the IoT — in terms
of how data are obtained and can be used, how long
data can be kept, limits on access by third (fourth or
fifth… nth) parties (the term ‘third party’ may prove
woefully inadequate in some cases). The information
collected from sensor systems may or may not be freely
accessible on the Internet (Open Data), and the data
transmitted may or may not cross the public Internet.
Given the high pervasiveness of the IoT’s impact, it is vital
that as more countries introduce policy frameworks, they
take into account the various factors and implications of
the IoT across different sectors. When all stakeholders
are included in active dialogue, the IoT represents a
promising opportunity for more coherent policy-making
and implementation.
6 “Regulation and the Internet of Things”, GSR-2015 Discussion Paper, available
at: www.itu.int/en/ITU-D/Conferences/GSR/Documents/GSR2015/Discussion_
papers_and_Presentations/GSR_DiscussionPaper_IoT.pdf
8

Extending our
Hyperconnected World
through the IoT
and Big Data
Photo credit: Marco Zennaro. Solar-powered, Wi-Fi enabled soil moisture node at the Asian Institute of Technology in Bangkok.
9

Defining the Internet of Things
After more than a decade of debate, discussion and
anticipation, the ‘Internet of Things’ (IoT) is finally
emerging. As early as 2005, the ITU noted that the
development of the Internet of Things as a function
of our hyperconnected world encompassed a set of
technological advances from different fields — specifically,
wireless and mobile connectivity, nanotechnology,
radio-frequency identification (RFID) and smart sensor
technologies.1
Advances in these technologies, when
combined, could help realize a miniaturized, embedded,
automated Internet of connected devices communicating
regularly and relatively effortlessly.2
Today, governments, businesses, and consumers are
using the IoT and Big Data to introduce new business
models, to improve the delivery of services, to increase
efficiency in production, and to enhance wellbeing
and human welfare. As with many other technologies,
vendors, implementers, operators, policy-makers and
regulators aim to maximize the benefits of deployment
while minimizing potential risks to security and privacy.
Widely disparate definitions of the IoT exist. The ITU
has defined the IoT as “a global infrastructure for the
information society, enabling advanced services by
interconnecting (physical and virtual) things based on
existing and evolving interoperable information and
communication technologies” (Recommendation ITU-T
Y.2060).3
The IoT clearly includes M2M (referring
specifically to communication directly between
devices, used in a vast array of applications and for
a variety of purposes4
), but broader definitions of IoT
technologies also include ambient intelligence and smart
environments.
1 ITU (2005). “ITU Internet Report 2005: The Internet of Things.” Available at:
www.itu.int/osg/spu/publications/internetofthings/ and www.itu.int/osg/spu/publi-
cations/internetofthings/InternetofThings_summary.pdf.
2 Opinion is divided on how many of these connected devices really need
to interconnect and communicate with each other at any point of time. Some
commentators suggest only 2% of devices need to communicate (for a specific
purpose). The ‘smart environment’ vision foresees nearly all devices within an
‘environment’ communicating.
3 ITU-T Recommendation Y.2060, note, s.8.4.
4 For example, Vodafone has defined M2M as “remote wireless data inter-
change between two or more devices or a central station that allows remote
monitoring and control of devices and processes.” See Vodafone press release
on 29 April 2010. “M2M : A new way of working.” Available at: http://enterprise.
vodafone.com/insight_news/2010-04-29_a_new_way_of_working.jsp
For example, ABI Research separates the “digital-first”
domain (e.g. PCs and mobile devices, designed mainly
as digital devices) from the “physical-first” domain (e.g.
humans and other, hitherto unconnected “things”).
ABI Research also notes that despite the ongoing
convergence of these two distinct domains, there is still
only one overarching Internet through which the domains
connect.5
A 2012 Machina-GSMA study distinguishes
“mobile-connected devices,” which connect directly to a
mobile network (usually via a SIM card) from the broader
“connected devices” market, which includes “short-
range devices” (e.g., devices using Wi-Fi, Bluetooth and/
or other connection technology).6
The UK regulator OFCOM differentiates between the two
related terms:
●● M2M: describing the interconnection (usually via
wireless technologies) of devices that would not
previously have had the ability to communicate (e.g.,
connected cars).
●● The IoT: a broader term describing the interconnection
of multiple M2M applications, often enabling the
exchange of data across multiple industry sectors
(e.g., the ability to manage traffic flow, reduce pollution
and improve health by combining data from a range of
transport, healthcare and environmental sensors).
Deloitte sees M2M as a category that has become
broader over time, encompassing all types of telematics
over cellular networks on trucks, smart utility meters,
eReaders, tablets and PC modems, but excluding
smartphones.7
They suggest that it is inappropriate for
M2M to include eReaders, tablets and PC modems
since, “although there is the occasional automatic
data upload or download, most of the traffic via these
devices is human-initiated and human-observed.” On
this basis, Deloitte excludes smart TVs, games consoles
and set-top boxes from its predictions. The research
consultancy GP Bullhound agrees that it is the automatic
5 https://www.abiresearch.com/market-research/product/1017637-inter-
net-of-everything-market-tracker/
6 http://www.gsma.com/newsroom/press-release/gsma-announces-the-busi-
ness-impact-of-connected-devices-could-be-worth-us4-5-trillion-in-2020/
7 Deloitte (2015). “Technology, Media and Telecommunications Predictions
2015,” page 6. Available at: http://www2.deloitte.com/content/dam/Deloitte/
global/Documents/Technology-Media-Telecommunications/gx-tmt-pred15-full-
report.pdf
10

initiation of traffic and data exchange that is the
distinguishing feature of M2M.8
The IoT is perhaps best understood as a set of related
technologies that can be used together to achieve
exciting ends, and it can be defined in terms of its
contributing technologies, including the use of sensors,
RFID chips, nanotechnologies and identification systems
(chips, cards, SIMs), among others. Overall, IoT and
various related technical developments (including
convergence, cloud services, data analytics and the
proliferation of sensors) are resulting in:
1. greater monitoring and measurement of humans,
machines and things; as well as
2. a shift from human-to-human communications
to M2M, something-to-everything, and everything-
to-everything communications.
3. Greater and more rapid awareness of and
information about status, function, and environment.
These different definitions of M2M, IoT, and what
constitutes a “network-enabled device” result in
widely varying estimates of the number of connected
devices — depending on whether mobile, tablets, PCs,
and wearables are included in definitions of ‘connected
devices’. It is unclear whether many of these estimates
include wearables.
Gartner predicts that there will be 6.4 billion connected
things in use worldwide in 2016.9
However, in mid-2015,
Cisco estimated that there were already 15.7 billion
“devices connected to the Internet” – including mobile
phones, parking meters, thermostats, cardiac monitors,
tires, roads, cars, supermarket shelves and many other
types of objects.10
8 GP Bullhound Technology Predictions 2015. Available at: http://www.gpbull-
hound.com/wp-content/uploads/2015/01/GP-Bullhound-Technology-Predic-
tions-2015.pdf
9 http://www.gartner.com/newsroom/id/3165317
10 Cisco, Visual Networking Index 2015. Available at: http://www.cisco.com/c/
en/us/solutions/service-provider/visual-networking-index-vni/index.html
Comparable estimates include:
●● 25 billion ‘networked devices’ by 2020 (ITU);11
●● 24 billion ‘connected devices’ by 2020 (Machina
Research in conjunction with GSMA);12
and
●● 26 billion deployed IoT devices by 2020, a thirty-fold
increase from 2009 (Gartner).13
These broad estimates are considerably higher than
Analysys Mason’s 2011 prediction that there will be
2.1 billion M2M device connections by 202014
, which is
based on a narrower definition of M2M.
However, other sources place this figure considerably
higher still, including the well-known Ericsson estimate
of 50 billion connected devices by 2020, a figure with
which the Hamilton Project (Figure 1, page 12) agrees,
after factoring in non-IP connected devices (such as
RFID tags). ABI Research estimates that the installed
base of ‘active wireless connected devices’ exceeded 16
billion in 2014, up 20% from 2013, and that it will more
than double to 40.9 billion by 2020.15
Based on their definition, Deloitte estimates that IoT
hardware could be worth around US$10 billion and IoT
services worth around US$70 billion in 2015 alone.16
Anecdotal reports from various firms corroborate the
market size and opportunity.17
ABI Research suggests
that IoT hardware and connectivity revenues are growing
at 10-20% annually, while apps, analytics and services
are growing at 40-50% annually. Based on rapid
growth, Gartner estimates that IoT product and service
suppliers could generate incremental revenue in excess
11 “ITU Predicts 25 Billion Networked Devices by 2020”, 28 September 2012,
http://www.v3.co.uk/v3-uk/news/2207590/itu-predicts-25-billion-networked-
devices-by-2020, via ACM TechNews.
12 GSMA, quoted at: http://www.gsma.com/newsroom/press-release/gsma-
announces-the-business-impact-of-connected-devices-could-be-worth-us4-
5-trillion-in-2020/
13 Gartner, Forecast: The Internet of Things, Worldwide, 2013, at https://www.
gartner.com/doc/2625419/forecast-internet-things-worldwide-
14 S. Hilton, Imagine an M2M world with 2.1 billion connected things… Analysys
Mason, 27 January 2011.
15 https://www.abiresearch.com/press/the-internet-of-things-will-drive-wire-
less-connect/
16 Page 6, Deloitte (2015), « Technology, Media and Telecommunications
Predictions 2015”: http://www2.deloitte.com/content/dam/Deloitte/global/Docu-
ments/Technology-Media-Telecommunications/gx-tmt-pred15-full-report.pdf
17 www.forbes.com/sites/aarontilley/2015/05/15/qualcomm-the-internet-of-
things-is-a-billion-dollar-business/; http://bits.blogs.nytimes.com/2014/10/09/
ge-opens-its-big-data-platform/?_r=0
11

1,000,000
100,000
10,000
1,000
100
10
1
1960 1970 1980 1990 2000 2010
Each new computing cycle typically generates around 10x installed base of the previous cycle
The cost of computing power is falling rapidly
Figure 1: Miniaturizing Multiplying – Getting Smaller More Numerous
10MM+Units
Minicomputer
Devices or users in millions; logarithmic scale
Devices/Users(MMinlogscale)
1MM+Units
Mainframe
100MM+
Units
PC
18+Units/Users
Desktop
Internet
108+ Units
Mobile
Internet
NumberofConnectedDevices(Billions)
Sources: Mary Meeker’s Internet Trends Report 2014 (top), available at: http://qz.com/214307/mary-meeker-2014-internet-
trends-report-all-the-slides/; The Hamilton Project, Brookings Institute, Ericsson (bottom).
$100,000,000
$10,100,000,000
$1,000,000
$10,000
$100
40
50
30
20
10
1960 1970 1990 2000
iPad2
2010
12.5 Billion
2013
15 Billion
1969
4
2000
200 Million1984
1,000
Gateway G6-200
IBM PC
Altair 8800
CostofEquivalentComputingPowertoaniPad2
(LogScale-2010Dollars)
2010 2020
12

Figure 2: The Intersection between IoT, M2M, Big Data and examples in
international development
Note: P2P: Person-to-Person; M2P: Machine-to-Person; M2M: Machine-to-Machine .
Source: Cisco Systems .
of US$300 billion by 2020, resulting in US$1 .9 trillion in
global economic added value .18
IDC forecasts that the
worldwide market for IoT solutions will grow from US$1 .9
trillion in 2013 to US$7 .1 trillion in 2020 .19
The Internet of Things (comprising connected devices
and connected environments, such as M2M and M2P)
and Big Data are separate, but related, concepts . There
is significant overlap between these two concepts,
especially where data are collected and transmitted via
connected devices and IP (Figure 2 below) . Person-to-
person (P2P) communications are direct interactions
between individuals, facilitated by IP, such as emails,
video conferencing, SMS and phone calls . Machine-to-
person (M2P) transactions include machine-generated
18 www .gartner .com/newsroom/id/2684616
19 www .idc .com/getdoc .jsp?containerId=prUS24903114
information communicated to individuals (such as
automatic text messages from one’s bank regarding
account updates) .
M2M communications include machine-generated data
transmitted to other machines that process the data
and determine which further actions may need to be
taken . For example, onboard vehicle sensors transmit
data on engine, transmission and wheel performance to
a central processing capacity, which then determines
at what point the vehicle may need to be serviced
to prevent a breakdown . Simply put, the IoT involves
machine-generated or machine-processed data that are
communicated over IP .
P2P
M2P
M2M
The Internet
of Things
Big Data
The Internet of Everything and
Everyone
e .g .: Tracking mobile signals
for population migration after
epidemic outbreaks (e .g .
Cholera/Haiti; Ebola/Liberia)
e .g .: Mobile money
transactions
e .g .: Paper census records
digitized and then analyzed
e .g .: Hand water
pumps equipped to
send text message
reports of faulty
pumps
e .g .: Aggregate water pump
flow and use data analyzed
to inform new pump location
decisions
e .g .: Networked
smoke and fire
sensors to transmit
warnings between
homes in densely
populated informal
settlements
e .g .: Aggregate data from fires in
settlements is analyzed to inform
urban planning decisions
13

These sub-categories in Figure 2 correspond roughly
with ABI Research’s categorization of the “Internet
of Everything”20
as a technology consisting of three
subsystems:
●● the Internet of Digital;
●● the Internet of Things (broadly corresponding with
M2M in Figure 2); and
●● the Internet of Humans (broadly corresponding with
Person-to-Person or P2P in Figure 2 above).
Another case in point is the purpose of IoT applications.
These technologies benefit developing countries, in
addition to their developed counterparts, in driving
social and economic advancement. Services ranging
from water and sanitation to healthcare, agriculture
20 ABI Research definition available at: https://www.abiresearch.com/mar-
ket-research/product/1017637-internet-of-everything-market-tracker/
and beyond can be improved in the developing world
through the implementation of IP-enabled sensors and
actuators. Governments, development organizations,
businesses and citizens in the emerging world are
already incorporating the IoT and Big Data analysis to
help alleviate some of the developing world’s most
pressing problems.
M2M is often used for telemetry, robotics, status tracking,
road traffic control, logistic services and telemedicine.
Vodafone notes that “M2M technology can be used for
[…] a huge range of applications including data collection,
remote control, offsite diagnostics and maintenance,
remote monitoring, status tracking, fleet management,
traffic control and security systems”.21
Figure 3 illustrates
21 Vodafone press release, “Delivering M2M communications for the global
business”, 30 April 2009, available from Vodafone press office at: http://enter-
prise.vodafone.com/insight_news/deliver_m2m.jsp.
Figure 3: From Individuals to Society – Examples of data generated by the IoT
Source: Adapted from the ITU Draft GSR Discussion Paper 2015, “Regulation and the Internet of Things”,
Professor Ian Brown, Oxford Internet Institute, University of Oxford, UK.
Note: Individual smartphones can be used to connect cars or control smart homes through tethering, so there is some crossover
between individual and car/home connectivity.
Level
IoT
Examples
Data
Intended
Audience
Smart phones
Wearables
Connected Cars
Health devices
Smart homes
Smart Cities
Smart Grids
GPS, Fitbits
Visa PayWave
Mastercard Paypass
Employee passes
Individual
Mobile money
Fitness data, GPS
location-based data
Individual person
Immediate friends/ family;
banks; employers
Intelligent Transport Systems
Event Data Recorders (EDRs)
Blood pressure monitors;
remote burglar/heating
systems
Community
Speed, distance, airbag,
crash locations/alerts;
Heart rate, blood pressure,
Diet, remote heating data
GP, health authorities;
health car insurance;
police, social networks
Smart metering;
Smart water meters
Traffic monitoring
Society
Electricity/water
consumption billing;
Traffic flow data
Authorities/regulators
Utility companies;
Other citizens
14

how IoT applications at the individual level (including
GPS location, payment systems and wearables) can be
aggregated up into applications for smart homes and
connected cars. At the macro level, three of the areas of
greatest IoT development and investment are smart cities
(where infrastructure and building systems could improve
the efficiency and sustainability of a range of urban
activities), smart power, and water grids.
Wearables are included in the definition of the IoT for the
purposes of this report, because most wearables either
communicate directly with another device (e.g. heart rate
monitors that connect via Bluetooth to a smartphone)
or they connect directly and upload data to the Internet.
This may sound like a large number of applications, but
according to the GSMA, pure M2M connectivity and
sensor networks may represent only a small subset of
some of the broader use cases for 5G connectivity.22
How the Internet of Things is Emerging
The strong growth currently observed in IoT applications is
attributable to several major underlying trends that are just
now coming to fruition:
●● The reduction in the cost of computing (including
sensors) and the growth of Wi-Fi are enabling factors
driving growth in IoT applications;23
●● Growth in mobile and the deployment of data-friendly
3G networks from 2001 onwards, as well as the
expansion of network connectivity across the world, and
from urban to rural settings (including Wi-Fi, but also
macro cell connectivity);
●● The rise of software development, partly attributable to
economies of scale; and
●● The emergence of standardized low-power wireless
technologies (as suggested by ABI Research24
).
22 GSMA Intelligence (December 2014), “Understanding 5G: Perspectives on
future technological advancements in mobile.
23 “Internet of Things By The Numbers: Market Estimates And Forecasts”,
Forbes, 22 August 2014, available at: http://www.forbes.com/sites/gil-
press/2014/08/22/internet-of-things-by-the-numbers-market-estimates-and-
forecasts/
24 https://www.abiresearch.com/market-research/product/1017637-inter-
According to the ITU GSR 2015 Discussion Paper on
regulation and the Internet of Things,25
one possible
explanation for why the IoT is advancing rapidly now
is that it is moving from a position where it delivers
incremental efficiency improvements to existing business
models to one where it positively impacts new business
models and processes as well.26
In terms of existing processes, the IoT can improve
and enable a broad range of applications — from more
efficient manufacturing, logistics, counterfeit detection,
monitoring of people, stock, vehicles, equipment
and infrastructure, to improved healthcare, traffic
management, product development and hydrocarbon
exploration.
In addition, the IoT is now also enabling the exploration
of new business models such as car and truck rental
clubs, whose members can book and use vehicles
parked around their neighborhood almost on-demand; or
“pay-as-you-drive” insurance based on driving patterns,
behavior, and risk. For marketers, the IoT enables brands
to gather more information about their customers, and
create “truly compelling, magical experiences.”27
Functionalities of the Internet of Things
Any IoT system includes a set of functions and
parameters (Table 1, page 17), many of which are
common to other types of connectivity as well. IoT
systems can be defined according to a set of criteria
in terms of their communications technologies, range,
necessary bandwidth, number of nodes, robustness,
reliability and/or power requirements (more often referred
to simply as ‘battery life’).
Functionality
For any IoT scheme, a number of trade-offs are involved
in performance, efficiency, reliability, robustness,
flexibility, power supply, scalability, interoperability, ease
25 ITU Draft GSR Discussion Paper 2015, “Regulation and the Internet of
Things”, Professor Ian Brown, Oxford Internet Institute, University of Oxford, UK,
26 ITU Draft GSR Discussion Paper 2015, “Regulation and the Internet of
Things”, Professor Ian Brown, Oxford Internet Institute, University of Oxford, UK,
27 Andy Gilder. Beyond wearables: brands could capitalise on the Internet of
Things. Memeburn, 6 June 2013.
15

of authentication, preservation of privacy, extensibility,
mobility support, and modularity. For example, trade-offs
may arise within the following:
●● Complexity versus robustness
(e.g. of sensors and connectivity modules);
●● Range, data throughput, and cost
(e.g. Wi-Fi vs LTE vs LoRa vs ethernet);
●● Battery life versus data throughput. According to Cisco,
new models of electrical generation and transmission
(including batteries, simple chemical reactions, energy
harvesting devices, etc.) will be needed to power the
multitude of new devices that will emerge.28
See Annex 2 for discussion on different battery life
characteristics of different connectivity modules.
●● High reliability levels may prove very important in
large-scale systems using hundreds of thousands of
28 Pepper, R. Garrity, J. (2014) The Internet of Everything: How the Network
Unleashes the Benefits of Big Data. Global IT Report 2014. WEF. http://www3.
weforum.org/docs/GITR/2014/GITR_Chapter1.2_2014.pdf
sensors, devices and/or readers (e.g. for seismic
monitoring networks around nuclear plants, but not
necessarily for RFID tags on livestock).
●● Low latency (the time required for round-trip data
transmission) becomes all the more vital for advanced
cloud computing applications (such as high-definition
video conferencing and industrial collaboration), where
any interruption or delay in data transmission can have
major consequences.29
The choice of final specifications among the different
options available depends on the aims of the deployment,
and the challenges to be solved. For example, Figure 4
defines some of the technical characteristics of several
different IoT applications.
Figure 4: Different IoT applications with Different Characteristics
Source: Handbook: Impacts of M2M Communications Non-M2M Mobile Data Applications on Mobile Networks, page 50.
ITU (Geneva, 2012). Available at: www.itu.int/md/T09-SG11-120611-TD-GEN-0844/en.
Example Applications
Data
volume
Quality of
Service
Amount of
signaling
Time
sensitivity
Mobility
Server initiated
Communication
Packet
switched only
Smart energy meters no yes yes
Red charging yes no yes
eCall yes no no
Remote maintenance no yes yes
Fleet management yes yes no
Photo frames no yes yes
Assets tracking yes yes no
Mobile payments yes no yes
Media synchronisation yes yes yes
Surveillance cameras no yes yes
Health monitoring yes yes yes
very low low intermediate high very high
16

Source: ITU.
Table 1: Functionality within IoT Technologies
Functionality Examples
1. Location and mobility
Fixed or mobile location (e.g. GPS, GPRS) or for monitoring the status of the engine in a
mobile car or on a flying drone.
2. Identification and addressing For example, IP addresses, IMEI, chips, smart cards, SIMs.
3. Topology, architecture and
node density and dispersion
This refers to the degree of dispersion of nodes within an environment. There may be many
close together (a dense environment) or few far apart. In a very densely connected
environment, an Internet of disparate connected devices may become a ubiquitous, fully
connected environment (e.g. a connected hospital, smart cities). Topology takes into
account the architecture and how nodes are constructed for reporting purposes
(e.g. zone architecture or parent-child tree architecture with hierarchies).
4. Purpose
Detection/reporting of status. Many different types of sensors exist (e.g. thermal, moisture,
optical, radiation, acoustic, kinetic, acoustic, chemical, position, instrumentation, motion,
level, GPS coordinates) for monitoring a range of variables associated with tracking position
(e.g. weather, climate, water, light, salinity, soils, vegetation, etc.) - see Figure 5 overleaf.
5. Efficient transmission/
exchange of information and
performance
Centralized or distributed communications via central or local servers, central analysis and
processing. In many developing countries with available mobile networks, communication
modules rely on wireless connectivity (GPRS, 3G, WiFi, WiMAX, etc.). Data communications
can be further broken down into other variables:
●● Range;
●● Routing: multi-hop or dynamic flooding;
●● Data volume and bandwidth needs – high or low;
●● One-way or two-way transmission capability;
●● The data may or may not be transmitted via the public Internet;
●● Latency and QoS of transmission.
6. Power system
(aka battery life)
Independently operated or back-up power system. The battery life may have an important
trade-off with the weight and/or complexity of the sensor. Some devices will need
constant, wired power.
7. Dormancy versus regularity
of communication
Some use cases for M2M require the connected device in the field to lie dormant for
periods of time, or to use ‘lean,’ regular, or ‘bursty’ signaling (to note, continuous transmis-
sion has zero dormancy).
8. Alert/warning system
Commonly found in systems which alert to natural disaster. This may be binary (working/
fault; heart beat versus no heart beat) or set for the detection of motion (seismometer)
or threshold (e.g. detection of pollution).
9. Automation
Automation/independent function – whether human initiation, monitoring or intervention
is required.
10. Frequency of visit/
replacement
For maintenance and/or obsolescence and replacement.
17

Sensors
Much of the functionality of the IoT and the data
transmitted in the M2M and M2P notions of the IoT are
determined by the nature of sensor measurement. Other
machines associated with the IoT include actuators,
devices that can be directed to perform a physical
activity such as opening an irrigation dam or closing a
livestock fence. However, in the context of development,
the majority of current applications utilize connected
sensors.
As with the decline in the cost of computing power,
the costs per unit of sensors have dropped steadily
over time. Today, sensors are found in many everyday
devices, and some of the latest smartphones come
with at least ten embedded sensors, for example: a
microphone to capture sounds, camera(s) to capture
images (front and back), a fingerprint sensor, global
positioning system (GPS), accelerometer, gyroscope,
thermometer, pedometer, heart rate monitor, light sensor,
touch screen, and barometer (not to mention the various
connectivity technologies such as Wi-Fi, Bluetooth, GSM/
CDMA, LTE and NFC).30
Crop and livestock management serve as primary areas
where connected sensors can provide data and facilitate
ease of use for farmers and ranchers dealing with
unpredictable climates. Sensor measurements also aid in
the detection and prevention of major natural disasters.
With pressure, temperature, and weather pattern sensor
technology, appropriate emergency preparedness
strategies can be used to brief, alert, educate and,
if need be, evacuate populations in high-risk areas.
Sensors equipped with accelerometer or seismographic
capability can warn populations in high-density urban
areas of impending earthquakes, tsunamis or typhoons.
Sensors are one of the primary modes of realizing the full
potential of value added to companies, communities, and
individuals that employ them for IoT purposes. In general,
sensors host a heterogeneous school of functions. They
can detect everything from changes in temperature and
humidity to the amount of force and pressure being
30 http://www.phonearena.com/news/Did-you-know-how-many-different-
kinds-of-sensors-go-inside-a-smartphone_id57885; http://web.stanford.edu/
class/cs75n/Sensors.pdf
simultaneously applied to thousands of products on a
manufacturing floor. Sensors can be broadly deployed to
overcome a host of challenges but, in some cases, they
may need to be highly customized. It is this customization
that enables sensors to provide real added value for IoT
and ICT4D initiatives.
Appropriate ICT sensor technologies that address
challenges surrounding primary healthcare, agriculture,
aquaculture, water treatment, and air quality can be
deployed in an efficient and cost-effective manner.
Figure 5 (page 19) illustrates some commonly found
sensors.
Photo credit: Marco Zennaro. Solar powered air quality sensor
in Cotonou, Benin, sending data via SMS.
18

Figure 5: Range of common sensors
Source: Harbor Insights.
Position/
Presence/
Proximity
Machine Vision/Optical
Ambient Light Motion/Velocity/Displacement
Flow
Acceleration/Tilt Temperature
Leaks/Levels Acoustic/Sound/Vibration
Force/Load/Torque
Strain/Pressure
Chemical/Gas
Electric/Magnetic Humidity/Moisture
Figure 5 highlights the range of common sensors
available on the commercial market, while Table 2
(page 20) segments sensors by example and use case:
●● Position, presence, proximity sensors such as the
Global Positioning System (GPS) and transponders can
provide information about location.
●● Motion, velocity, displacement include kinetic
sensors that can measure movement, e.g. for wind
and water direction, speed, period, precipitation,
barometric pressure and motion detection, as well
weight measurement (e.g. for animals or loads).
Accelerometers measure movement and gyroscopes
measure tilt, and digital vernier calipers can measure
the size of cracks.
●● Temperature sensor measurements are for ambient
air or substances, such as water or soil. Similarly,
humidity/ moisture can measure the presence of air,
water and other substances, including toxic gases.
●● Acoustic, sound and vibration sensors can be used
to identify wildlife (for research or monitoring) as well
man-made noise (including for gunshots, chainsaws
and motor vehicles) to aid in anti-logging and
anti-poaching efforts.
●● Chemical and gas sensors are used to measure
oxygen, nitrates, pH, salinity, black carbon and carbon
dioxide. Additional types of sensors can measure leaks
and levels of fluids and gases, or levels of electricity
and magnetism which can provide information about
instrumentation such as battery life.
●● Flow sensors are used in water pipes, pumps, bodies
of water, and air.
●● Force/load/torque/strain/pressure sensors can be
used for weight measurements as well as in monitoring
infrastructure (such as traffic flow over bridges).
●● Machine vision and optical ambient light sensors
can measure different elements of visual bandwidth
(including color, intensity and/or clarity).
●● Radiation sensors can measure radiation levels and be
used remotely to measure sites for nuclear spills.31
31 Interview with John Waugh, April 13, 2015.
19

Table 2: Sensor Types, Functionality and Examples
Measurement Functionality Sensor Examples Use Cases
Proximity/Position
Detect and respond to
angular or linear position
of device
RFID, linear position sensors,
GPS position sensors,
location finding
Land management; natural
resource/wildlife
management; illegal activity
tracking
Motion/Velocity/
Displacement
Detect movement outside
of component within sensor
range
Ultrasonic proximity, optical
reflective sensors, Passive
infrared (PIR), inductive
proximity, accelerometers,
gyroscopes
Emergency preparedness;
land management; illegal
activity tracking
Weather/Temperature
Detect amount of heat in
different mediums and
metrics
Thermometers, resistant
temperature detectors,
thermocouples, infrared
thermometers
Water access; water
treatment; agriculture; emer-
gency preparedness; land
management
Acoustic/Sound/Vibration
Detect decibel level sound or
seismic disturbances
Seismography, firearm
sensors, commercial security
Emergency preparedness;
illegal activity tracking
Flex/Force/Pressure/Load
Detect force(s) being exerted
against device
Pressure monitors, capacitive
transducers, piezoresistive
sensors, strain gauges
Natural resource
management
Chemical/Gas/Electric
Detect chemical, gas, or
electrical changes in
composition of substance
DC/AC electrical current
sensors, voltage transducers,
smart home sensors, humidi-
ty monitoring
Agriculture; natural resource
management; health;
water treatment
Light/Imaging/Machine Vision
Detect color and light shifts
through digital signaling
Real-time temperature
monitoring (infrared)
Health
Prototyping feasible IoT solutions is aided by the ubiquity
of sensor devices in production and the low cost of
entry for users. A complete state-of-the-art IoT sensor
suite can be deployed for approximately US$250. In
fact, the overall cost of sensor installment, deployment,
and support has decreased by a hundred times over
the past decade.32
This low cost of entry makes sensor
technology highly accessible to aspiring IoT developers,
32 https://gigaom.com/2015/01/25/declining-sen-
sor-costs-open-up-new-consumer-applications/
and enhances innovation in this space. Low-cost sensor
technology must be used in tandem with microprocessor
and circuit devices.
At a minimum, a single sensor is usually deployed with a
single-board micro-controller computer device, such as
a Raspberry Pi or Arduino board.33
33 http://sweden.nlembassy.org/binaries/content/assets/postenweb/z/zweden/
netherlands-embassy-in-stockholm/iot_roadmap_final_draft_0309145.pdf
Source: “Roadmap for the Emerging Internet of Things”, Carre Strauss; and Cisco authors’ adaptation.
20

Highest Cost
Lowest Cost
$150-$1000+
$50-$150
$0 - $50
These micro-controller devices serve as essential IoT
purpose-built platforms for the end-user and often
require the end-user to be familiar with software
engineering principles to achieve effective functionality.
Not all ICT stakeholders need access to full-fledged
sensor suites to meet their solutions. Solution-
appropriate sensors can be sourced and purchased for
US$50-150 depending on the necessary functionality
needed to meet various ICT4D challenges, as noted in
Figure 6.
Deploying sensors to meet ICT4D solutions can be
cost-effective. Realistically, any value gained from
utilizing IoT sensor technology could be realized within
four to six months post-deployment. For example, a
temperature, wind, and precipitation sensor suite in a
rural agricultural community could collect and analyze
weather data over a three-month timeframe that could
be used to better prepare the community for upcoming
weather patterns and result in better crop and land
management moving forward. It is important to note that
the real value from using IoT-enabled sensors derives
●● Chemical/Gas
●● Electrical/Capacitive
●● Pressure/Load/Weight
●● Proximity/Position
●● Long-term install/deployment
●● Industrial scale deployment
●● Extreme accuracy/precision
●● Typically large enterprises
●● Ease of solution interoperability
●● Water Treatment/Flow
●● Weather/Temperature
●● Motion/Velocity
●● Acoustic/Sound/Vibration
●● Light/Imaging
●● Proximity/Position
●● Flex/Force/Strain
●● Residential/commercial
●● Advanced development kits
●● Consumer-based support
●● Cloud partnership capability
●● Fast deployment
●● Medium infrastructure required
●● Low-Medium accuracy/Precision
●● Water Treatment/Flow
●● Weather/Temperature
●● Motion/Velocity
●● Acoustic/Sound/Vibration
●● Light/Imaging
●● Single function
●● DIY/Prototyping often needed
●● Limited without other hardware
●● Requires basic equipment
●● Geared towards amateurs
●● Singular functionality
●● No infrastructure required
Sensor TypeFunctionality
Figure 6: Sensors by retail cost
Source: Authors; see annex 3 for further details.
21

from the ability of experts, analysts, and communities to
use their collected data effectively and efficiently to drive
current ICT-centered projects and to predict and plan
for future challenges that could be addressed with IoT
technologies .
Connectivity
The connectivity requirements of different types of IoT
networks vary widely, depending on their purpose and
resource constraints . A range of different wireless and
wireline technologies can be used to provide full IoT
connectivity (Figure 7) . IoT devices communicate using
a range of different communication protocols, which may
include: short-range radio protocols (such as ZigBee,
Bluetooth and Wi-Fi); mobile networks; or longer-range
radio protocols (such as LoRa) . These technologies can
be segmented based on wireless versus wireline, and the
wireless technologies can be grouped by personal area
network (WPAN), wireless local area network (WLAN) or
wide area network (WWAN) technologies .
Each technology has distinct characteristics, including
the range of their signal, the extent of their data
throughput (or bandwidth), and the power needs of the
communications device (or battery life), among other
attributes . Annex 2 discusses these in detail .34
ABI Research notes that technologies such as
Bluetooth and ZigBee are helping drive node/sensor
implementations, while Wi-Fi or cellular technologies are
providing the backbone for data transfer to the cloud .35
In terms of individual technologies:
● Bluetooth is a high-speed, short-range and high data
rate but low battery life wireless technology that can
replace wired devices .
● Radio protocols such as Ultra-Narrow Band can
provide longer-range coverage (useful for smart city
applications such as video monitoring) .
34 See also http://www .eejournal .com/archives/articles/20150907-lpwa/
35 https://www .abiresearch .com/market-research/product/1017637-inter-
Figure 7: Comparing IoT Connectivity Technologies
Note: Non-exhaustive
Source: Cisco Systems . See also Annex 1 .
Wireless-
Bandwidth
Personal Area
Networks (WPAN)
Wireline
ANT+, Bluetooth, 4 .0 LE
RFID, NFC
802 .11 .4, ZigBee
Local Area Networks (WLAN)
Wi-Fi
Wide Area Networks
(WWAN)
LoRa, Weightless, Dash 7
WiMax, 2G, 3G
4G/LTE, Satellite
Copper/DSL
Coaxial
Fiber
Range
short to long
narrow to broad
Battery Life
short to long
22

●● Low-rate wireless personal area network (LR-WPAN)
is a low-cost, low-power and low data rate wireless
technology used for inexpensive mobile devices.
●● Devices communicating over kilometres may access the
300 MHz to 3GHz spectrum range;
●● Centimetre or millimetre contactless transactions may
use Near Field Communications (NFC) in 13 MHz or
Extremely High Frequency (EHF) bands.
●● Some IoT applications may also make use of AM/FM
bands in the Very High Frequency (VHF) range.
Until at least 2010, GSM remained the most widely used
technology for M2M,36
especially in areas with coverage
where advanced data transmission had been needed.
However, given that many modern multi-sensor networks
only require occasional connectivity with minimal
throughput and signaling load, GSM connectivity may not
be the most appropriate technology for certain projects.
When sensor networks send SMS at regular intervals,
costs can accumulate very quickly. Depending on
the nature of the sensor network and the terms and
conditions of the mobile contract, widespread use of
SMS may require negotiation of a separate contract for
IoT traffic. For example, in one project in Indonesia, the
mobile operator determined that SMS was being used for
“business” rather than for “personal” use and changed the
fee structure accordingly. There is a risk of bill shock and/
or lock-in with a particular provider or providers.
Wi-Fi is a short range wireless technology often used
in mobile devices (e.g., PDAs, laptops, tablets, etc.).
However, Wi-Fi is playing an increasingly important role in
the IoT, with Wi-Fi chips embedded in portable computers
and smartphones able to operate on a license-exempt
(unlicensed) basis37
and with the majority of the upgrade
costs falling on consumers rather than operators. By 2011,
one in ten people were using Wi-Fi, while today, Wi-Fi is
in 25% of homes around the world, with two billion Wi-Fi
devices sold in 2013.38
36 World Bank Broadband Strategies Handbook (2011).
37 The ITU has designated the 2450 MHz and 5800 MHz bands for industrial,
scientific, and medical (ISM) applications that “must accept harmful
interferences.” This is often interpreted to mean that they are considered
unregulated. See Frequently asked questions on the ITU-R website.
Available at: www.itu.int/ITU-R/terrestrial/faq/index.html#g013.
38 Wi-Fi Alliance. Available at: www.wi-fi.org/organization.php
The FCC’s expert IoT Working Group has predicted that
IoT will add significant load to existing Wi-Fi and 4G
mobile networks.39
Regulators need to give continuing
attention to the availability of spectrum for short-range
IoT communications, and the capacity of backhaul
networks, as well as encouraging the roll-out of small-cell
technology and 4G. Assuming these conditions are met,
the Working Group does not expect that new spectrum
will need to be explicitly allocated to IoT communications.40
Cisco observes that spectrum requirements include:
narrowband and broadband frequencies; short-haul and
long-haul spectrum; continuous data transmission and
short bursts of data transmission; and licensed spectrum
in addition to license-exempt spectrum.
Ensuring device connectivity and sufficient bandwidth
for wireless sensors requires careful planning.41
It is the
combination of these different qualities within Big Data
capacity that may offer the most exciting opportunities.
For example, location information may be combined with
status information to provide real-time information on
an evolving situation. There are huge new opportunities
opening up by improved access to and use of Big Data
techniques, which offer learning opportunities to improve
real-world processes and enhance decision-making
over the short-to long-term in healthcare, education,
emergency services and disaster response.
When integrated into an early warning system, real-time
data can be used to forecast potential outbreaks of
violence or natural disaster. Text analysis of social media
data has the potential to reflect growing tensions in a
region given high unemployment or political frustrations.42
Light emission data collected via satellite-produced
remote sensing images can also be analyzed to estimate a
country’s GDP in real-time.43
39 US FCC Technological Advisory Council IoT Working Group, Spectrum: Initial
Findings, FCC TAC meeting update, 10 June 2014. Available at: http://transition.
fcc.gov/bureaus/oet/tac/tacdocs/meeting61014/TACmeetingslides6-10-14.pdf
40 US FCC Technological Advisory Council IoT Working Group, Spectrum: Initial
Findings, FCC TAC meeting update, 10 June 2014. Available at: http://transition.
fcc.gov/bureaus/oet/tac/tacdocs/meeting61014/TACmeetingslides6-10-14.pdf
42 http://www.unglobalpulse.org/projects/can-social-media-min-
ing-add-depth-unemployment-statistics
43 ITU (2014). “Measuring the Information Society.”
23

Applications across
Different Sectors
in Development
Photo credit: Juozas Cernius/American Red Cross.
The photograph shows the installation of a smart home sensor network for fire detection in informal settlements in Nairobi, Kenya.
24

The Internet of Things in a Developing
Country Context
This section examines deployments of IoT technologies
in developing economies, covering what has worked
and formulating summary conclusions on key issues to
consider when extending the IoT to the billions of people
living in the developing world. The following specific
considerations may apply:
1. Increasingly observed in developing countries,
more of the population has access to basic
telecommunication network coverage than has
access to fundamental services such as electricity,
running water and basic sewage facilities. The ITU
estimates that, in 2015, over 95% of the world’s
population resides within the coverage area of
a 2G mobile-cellular network (and 69% under a
3G network).1
Figure 8 (page 26) compares and
contrasts urban and rural areas in sub-Saharan
Africa by their access to electricity, water and mobile
coverage.
2. Economic sectors and processes in developing
countries are more labor-intensive and may lack
supporting processes (e.g. agricultural systems may
not use technology-driven crop management, pest/
disease control or quality management systems).2
The Macrothink Institute notes that the information
requirements of productive systems in developing
countries are likely to be very different from those
in developed countries, meaning that monitoring
systems for developing countries are likely to need
different design requirements and
technological frameworks.3
3. Lack of resources means that simpler, more
cost-effective solutions may prove more effective
in a developing country context. For example,
using a wireless wide area network – WWAN – for
communication could lower the cost of M2M modules
1 “ITU Facts and Figures – The World in 2015.” Available at: www.itu.int/en/
ITU-D/Statistics/Pages/facts/default.aspx
2 Karim, Anpalagan, Nasser Almhana (2013). “Sensor-based M2M
Agriculture Monitoring Systems for Developing Countries: State and Challenges.”
Macrothink Institute.
3 See Footnote 2.
by using white space spectrum rather than other
networks with high-speed capabilities, according to the
GSMA,4
in areas where there is low interference from
analogue broadcasting channels.
4. Connectivity may begin with essential applications
only, which could be introduced initially on a small
scale and might not always become fully integrated
(e.g. as suggested by ‘greenshoots’ isolated pilots in
eHealth and mHealth).
5. More constrained resources and fragile environments
may make populations in developing countries
inherently more vulnerable to natural disasters.
For example, the Red Cross believes “emerging
technologies will play a particularly important role
in amplifying efforts to facilitate community-level
knowledge and health, connection, organization,
economic opportunities, access to infrastructure and
services, and management of natural resources”,
and has convened a Global Dialogue on Emerging
Technology for Emerging Needs of general
relevance.5
This does not mean that the developing world lacks
the prospects or potential to develop great applications
itself — just that deployments may differ from developed
country contexts in their purpose, resilience or supporting
infrastructures. In developing countries, for example,
deployments may be more likely to be made in isolation
and independent of supporting infrastucture. The much-
cited application of M-Pesa introduced by Safaricom
in Kenya began essentially for unbanked consumers,
compared with the development of wireless payment
systems in industrialized countries, where mobile money
transfers may accompany or complement the formal
banking system.
Fixed-line infrastructure is also often more prevalent in
developed country contexts, where IoT applications can
run off high-capacity fixed line infrastructure, compared
to developing countries, where IoT or sensor networks
4 http://www.gsma.com/mobilefordevelopment/wp-content/uploads/2013/01/
Sustainable-Energy-and-Water-Access-through-M2M-Connectivity.pdf
5 “A Vision for the Humanitarian Use of Emerging Technology for Emerging
Needs: Strengthening Urban Resilience.” Available at: https://drive.google.com/
file/d/0B1vf6TLGIC0yZk9UU2t2UGFmNlE/view?pli=1
25

Figure 8: Access to Energy, Water GSM Population Coverage in Sub-Saharan
Africa
Rural Urban
GSM Population Coverage
Electrification Rate
Improved Water Access
Total Population
User
88%
83%
58%
12%
49%
60%
Source: GSMA, “Sustainable Energy Water Access through M2M Connectivity. http://www.gsma.com/mobilefordevelopment/
wp-content/uploads/2013/01/Sustainable-Energy-and-Water-Access-through-M2M-Connectivity.pdf.
are most likely to be used in conjunction with mobile
infrastructure.
IoT interventions are increasingly common in advanced
economies. Wearable sensors in watches, bracelets and
even clothes can help users monitor their vital signs and
improve their health and wellbeing. In homes, offices
and factories, sensors can detect when rooms are in and
out of use, enabling more efficient heating and lighting
and helping improve working conditions. In many more
environments, smart meters can coordinate the energy
consumption of appliances to smooth out variations in
overall energy consumption and achieve more effective
use of variable renewable energy sources.
In developing country contexts, a range of uses
and applications of IoT technologies first started to
appear a decade ago. Impactful IoT interventions in
development either improve efficiency (achieving similar
levels of impact with fewer resources) or effectiveness
(increasing impact with similar levels of resources).6
IoT applications could help promote monitoring and
evaluation, and achievement of nearly all the existing
Millennium Development Goals (MDGs) and post-2015
Sustainable Development Goals (SDGs) (Table 3, pages
39-40). For example, sensors in agricultural fields are
monitoring soil conditions and moisture levels. RFID
tags are helping farmers provide more personalized
6 James Bon Tempo. Available at: http://linearityofexpectation.blogspot.
com/2015/02/the-purpose-of-ict4d-in-one-diagram.html
26

Figure 9: The Virtuous Circle of Development Impact
Source: Cisco Systems.
Research
Service
Delivery
Policy
Formulation
Monitoring
Evaluation
care for their livestock. Connected thermometers are
monitoring vaccine delivery and storage in real-time.
Cameras and sensors in smartphones and tablets are
allowing healthcare workers to provide remote diagnosis
of disease. And off-grid solar systems, monitored via
SMS, are bringing affordable electricity to lower income
families.
In tackling global development challenges, IoT
interventions are being utilized across the full spectrum
of development activities (Figure 9). Figure 10
(page 28) highlights how Big Data capacities enable both
real-time monitoring and response, and also predictive
pattern data analysis, facilitating a shift in strategy
from reactive to proactive. Academic researchers are
deploying sensors to improve research on increasing
agricultural yields, for example. Public policy is being
informed by data collection on community water usage.
Healthcare
The IoT has the potential to improve health and wellbeing
through greater efficiency and improved care in existing
healthcare settings, by enabling greater use of remote
telehealth provision, and enabling individuals to monitor
their own health day-to-day, improve wellbeing and
better manage conditions (such as stress, encouraging
exercise and healthy eating), diagnose medical
conditions more quickly and promote treatment regimes.
In terms of preventative care, IoT fitness devices such as
Fitbits, and movement sensors now built into many new
smartphones, enable many individuals to monitor and
track themselves, which generally promotes healthier
lifestyles. For example, Apple and Google have added
features to their latest smartphone operating systems to
integrate health sensor devices and promote users to
monitor their own health data using non-specialist health
tracking apps.
27

Figure 10: Areas of Highest Potential Impact across Different Sectors
Source: Naef et al. (2014), quoted in the “Measuring the Information Society 2014” report, ITU.
Ex-post Current Future
Evaluation
and Assessment
Measurement and
Real-time Feedback
Prediction
and Planning
Financial
Services
Mobile
money
agent
placement
Algorithmic
fraud detec-
tion
Social
network
analysis
marketing
Agent
network
monitoring
Enhanced
credit
scoring
Algorithmic
liquidity
needs
prediction
Economic
Develop-
ment
Income
and poverty
assessment
Mapping
social
divides
GDP
estimates
through
mobile data
Migration
monitoring
Text analysis
economic
downturn
prediction
Text analysis
commodity
fluctuation
prediction
Health
Assessment
of mobility
restrictions
Disease
containment
targeting
Migratory
population
tracking
Predicting
outbreak
spread
Agriculture
Mobile data
to track food
assistance
delivery
Geo-tar-
geted links
between
suppliers/
purchasers
Pests, bad
harvest
alerts
Agricultural
yield/shock
predictions
Commercial
Campaign
effective-
ness
Social
network
delineated
market
areas
Predictive
algorithms
to anticipate
product
churn
Social
network
targeted
marketing
Other
Post-disas-
ter refugee
reunification
Sentiment
analysis
of public
campaigns
Urban
planning
Mobile
disaster
relief
targeting
High
frequency
surveys
Crime
detection
Social
unrest
prediction
High Medium Low Pilot identified
28

As for chronic conditions, people with type 1 diabetes
may soon be able to set their insulin doses by
smartphone. Researchers are testing a “bionic pancreas”
pump that is inserted under the patient’s skin. When
paired with an app and a small chip, this device is
capable of tracking blood sugar levels and adjusting the
amount of insulin and glucagon (another hormone that
controls blood sugar) on its own. A key study is slated
for 2016 and the researchers, who are based at Boston
University and Massachusetts General Hospital, plan to
submit the device for US FDA approval in 2017.7
In developing countries, one innovative way sensors
are being used is to monitor the ‘cold chain’ delivery
of vaccines, particularly to remote and rural areas.
Nearly one-fifth of children in the developing world
go unvaccinated each year. One of the contributing
factors to this problem and a large hurdle for healthcare
providers is vaccine spoilage, as many vaccines need
to be stored at temperatures between 2 and 8 degrees
Celsius. With over 200,000 vaccine refrigerators in use
in the developing world alone, most of them in harsh and
remote environments, keeping the cold chain up and
running is a major challenge.
A number of projects have focused on monitoring the
cold chain, and one organization in particular, Nexleaf,
has been working on remote sensing products for use
in difficult environments.8
In many countries, refrigerator
temperatures are tracked and recorded by hand, often
with delays in collecting the records. Now, using IoT
technologies, Nexleaf’s ColdTrace system monitors and
records the refrigerator temperatures and send out SMS
alert messages whenever there is a problem (i.e., when
temperatures rise above a predetermined threshold).
Nexleaf has developed a mobile-enabled temperature
sensor device that uploads data to a cloud-based serve
in near real-time via GPRS or SMS. The server sends
regular messages to designated recipients regarding
current temperatures, and also warnings when the
temperature exceeds critical thresholds.
7 www.webmd.com/news/breaking-news/future-of-health/default.
htm#wireless-medicine-toc/wireless-medicine
8 Interview with Nexleaf on March 23, 2015. http://www.nexleaf.org
Cellular networks are being used to transmit this data
due to their wide availability and low costs. For example,
in India, monthly cellular connectivity for connected
temperature monitor devices are in the range of around
a dollar a month or less per fridge. The most expensive
component is often the cell radio, while conversely,
the temperature sensors are relatively cheap. In some
cases, advantageous mobile cellular terms can be
negotiated, resulting in reduced operating costs. The
wireless sensor device sends temperature data to
Nexleaf servers and alert messages are sent to vaccine
handlers and managers, if vaccine doses are in danger of
spoiling. A weekly and monthly summary of refrigerators’
temperatures is also sent via the website, so managers
can understand how well equipment is functioning.
These data can be used at the local and district level,
as well as being aggregated up to or by the Ministry
of Health at the national level to determine how to
allocate limited maintenance and equipment resources,
and where vaccine doses can be safely delivered (i.e.
to ensure a batch of vaccines is not dispatched to a
broken refridgerator). Data analytics are available on the
Nexleaf Dashboard to help understand managers where
problems are arising in power supply, and how to resolve
these challenges. Another example of these types of
interventions is the SmartConnect project, which focused
on developing a “communication appliance” to improve
the reliability and performance of the “cold chain.”9
IoT technologies are also being used to address
immediate challenges in humanitarian response, such
as the Ebola outbreak in West Africa. The United States
Agency for International Development (USAID) has
supported and employed IoT solutions via connected
wearable technologies. Sensor Technology and Analytics
to Monitor, Predict, and Protect Ebola Patients (or
STAMP2 for short) has been tested on Ebola patients
in the United States and is being scaled up to meet the
needs of government agencies such as USAID for its
Ebola treatment strategy in Liberia.
STAMP2 collects patient data, including ECG, heart rate,
oxygen saturation, body temperature, respiratory rate,
and position. These data are sent to a central server or
9 http://homes.cs.washington.edu/~eorourke/papers/smart_connect_nsdr.pdf
29

platform so they can be monitored and analyzed over
a long period of time and alert physicians of abnormal
changes in a patient’s behavior or health. The STAMP2
sensor will be deployed in Ebola-stricken areas using
a connected health patch, or “Smart Band-Aid.” The
fully equipped sensor-enabled band-aid is estimated
to cost approximately US$100 with a maximum battery
life of ten days, making it ideal for use in field-based
Ebola treatment centers. Deploying solutions such as
the STAMP2 sensor can improve the Ebola response
initiative at-large by decreasing emergency response
time in critical areas and enabling emergency responders
to detect Ebola patients earlier and monitor them more
efficiently.10
Other examples include the use of remote
diagnostics systems that allow for community health
workers to take measurements from patients and transmit
the data to doctors or specialists elsewhere.11
10 http://mobihealthnews.com/40564/scripps-wins-usaid-grant-to-monitor-
ebola-patients-with-medical-wearables/ and http://www.biospectrumasia.com/
biospectrum/news/220757/usaid-unveils-wearable-technologies-tackle-ebola
11 http://www.ictworks.org/2015/07/31/5-mhealth-innovations-using-mobile-
phone-extensions-and-wearables
Water and Sanitation
Nearly one billion people in the world lack access to safe
drinking water, while some two billion have inadequate
access to sanitation facilities. Currently, some of the most
extensive uses of the IoT in developing countries are in
projects where the objectives include the improvement of
clean water delivery and/or sanitation.
In Bangladesh, a biosensor network of 48 manual
arsenic sensors is being used to monitor water quality.12
In Jiangsu, China, water supply is being monitored by
adding IoT sensor devices at key points to register
data on water usage and flow rates.13
In India, Sarvajal
has developed low-cost reverse osmosis technology
to provide clean water in rural areas, as well as smart
meters to remotely monitor the quality and quantity of
water. Additionally, a wireless sensor network (WSN) is
being used in the country to improve water management
12 http://users.ictp.it/~mzennaro/WSN4D.pdf
13 www.chinamobileltd.com/en/ir/reports/ar2010/sd2010.pdf
Figure 11: Monitoring the Movements of People during the Ebola Outbreak
Source: http://eprints.soton.ac.uk/370053/1/CommentaryEbolaOutbreak.pdf
The figure shows regional travel patterns in West Africa based on mobile phone data and demonstrates
how anonymized call detail records (CDRs) can inform healthcare response activity in the event of an
outbreak of infectious disease, such as Ebola.
30

in poor and semi-arid areas. The wireless COMMON-
Sense Net has been deployed over a small area of two
acres to measure temperature, humidity, ambient light,
and barometric pressure in rural Karnataka. Soil moisture
has been measured with a special probe since April
2005.14
Data from the sensors are visualized on the
project’s website for real-time monitoring.
In Africa, water service reliability is closely correlated
with extreme poverty and water insecurity in rural areas.
Around one million hand pumps supply water to over
200 million rural water users across the continent, yet
as many as one third of all hand pumps are thought to
be broken at any given time, with 30-70% of pumps
breaking within two years. The Smith School Water
Programme at Oxford University launched a 12-month
‘smart hand pump’ trial in Kyuso, Kenya, in 2013 in an
attempt to resolve problems related to broken water
pumps and to test a new maintenance model for
universal and reliable water services.15
14 http://itidjournal.org/itid/article/viewFile/244/114
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=arnumber=4085509
15 www.smithschool.ox.ac.uk/library/reports/SSEE_Rights%20to%20Results_
FINAL_March2014.pdf
Using a basic accelerometer, similar to that found in
a mobile phone, to capture movement of the pump
handle, the team developed a robust transmitter which
fits into the handle of existing hand pumps, opting for
“field readiness” over technological sophistication (e.g.
using SMS rather than 3G to transmit data), as their
need for more robust, reliable data outweighed that
for data with advanced functionality. The team opted for
non-rechargeable batteries, with a replacement lifetime
longer than the pump maintenance cycle. An advantageous
mobile package and efficient use of the SMS message
format has resulted in relatively affordable data costs.
From the initial question of whether the pump was working
or not, the team quickly realized that they could remotely
monitor a host of data relating to water supply and demand,
including hourly flow rates, usage data, hand pump
performance, seasonality, and peak periods for demand.
Based on these data, a more evidence-based approach to
policy decision-making has been achieved. It is no longer
the village that “shouts loudest” or that has the best social
connections that ultimately receives service. The project
has been able to provide evidence about where and when
the greatest needs for water are experienced.
The team emphasizes that the project has led to a shift
in mindset. Although the Kenyan water regulator had
an existing mandate for regulating water supply in rural
areas, it was not able to engage effectively, as it lacked
concrete data about the situation. Now that more data
are forthcoming, the water regulator is better able to
manage resources. Performance-related pay has also
been introduced for maintenance staff, who now know
that pumps are being monitored remotely, and who are
generally more keenly engaged and responsive in repairing
pumps.
Although project staff were initially worried about
vandalization of the equipment, once people saw that the
technology worked and that it contributed to the pump
being repaired more quickly, local social structures of
respect and trust extended to include and protect the
transmitter. Due to these factors, over its lifetime, this
project has helped achieve:
●● a ten-fold reduction in hand pump downtime (measured
by the number of non-functional days);
Photo credit: Tim Foster, Rob Hope, Johanna Koehler and
Patrick Thomson. Sensor enabled village hand water pump in
Kyuso District, Kenya.
31

●● a shift to 98% of hand-pumps functioning (up from
67%);
●● a more fair and flexible payment model contingent upon
service delivery; and
●● new and objective metrics to guide water service
regulatory reform.
MoMo is a similar, but fully mobile, device with a sensor
that collects data to track infrastructure and improve
accountability in the developing world. The device
identifies where pumps are broken and alerts repair teams
to fix them. Data from MoMos can also help communities
monitor the effects of infrastructure projects and inform
future investments.16
In Rwanda, SWEETSense uses sensor technology
developed by Portland State University’s SWEETLab
(Sustainable Water, Energy Environmental Technologies
Laboratory) to monitor pump performance and water flow,
notifying technicians via SMS and emails.17
SWEETSense
technology uses sensors to provide continuous data
on usage and performance of programmes in water,
sanitation, household energy and rural infrastructure
programmes with diverse partners including USAID, the
UK’s DFID, Mercy Corps, the Lemelson Foundation, Gates
Foundation and DelAgua Health in India, Nepal, Indonesia,
Kenya, Rwanda and Haiti. Indeed, SWEETSense
technology has been used for:
●● Water pumps in Kenya;
●● Cooking stoves in India;
●● Latrine monitoring in Bangladesh; and
●● Water filters for hand-washing stations in Indonesia.
These sensors use Wi-Fi or cellular via GSM using a
local SIM card (in East Africa, Airtel or MTN or Safaricom
offer data plans for around US$6-10 a month to transmit
data). The data is then integrated into SWEETData™,
an Internet database monitoring summary statistics on
performance and usage to front-end users. Sensors
currently cost more than US$100, but this should fall
relatively quickly, as demand increases.
16 Interview with WellDone.org/ Momo on March 24, 2015.
https://www.welldone.org/
17 http://newsroom.cisco.com/feature/1556125/Sen-
sors-Change-Lives-in-Developing-Countries
For order/manufacture volumes of several hundred
thousands of sensors at a time, price reductions can
begin to be realized, which will help boost scale.
In Rwanda, sensors may add 10% to the cost of a hand
pump but may enable uptime to increase by some
80-90%, significantly reducing the cost per unit of
water per 10,000 litres delivered. Sensors are subjected
to very harsh conditions, typically resulting in some
12-18 months of battery life. In Kenya and Rwanda, the
regulatory challenges have proven relatively limited so
far, and the regulations for large-scale connectivity are
nascent.
Water flow sensors are also being used in sanitation
projects focused behavior change. For example, in one
water, sanitation, and hygiene programme in Indonesia,
flow sensors were combined with motion detectors to
measure the impact of behavior change training, with
the aim of increasing hygienic actions (i.e., washing
hands after latrine use). The study found community
participants were washing hands after latrine usage, but
that the survey responses (those who indicated they
wash their hands after using the latrine) were significantly
higher than the data captured by the motion sensors,
suggesting that over-reporting had occurred in the verbal
and self-reported surveys.18
Similar techniques can
be used to remind staff of basic hygiene techniques in
homes, clinics, and hospitals.
Agriculture
In agriculture, IoT technologies can be used to increase,
protect, and optimize crop production, as well as improve
the storage and distribution of food. Growth in agricultural
productivity over the last fifty years has been much
slower in developing regions of the world, in part due
to large capital costs.19
Similarly, gathering and utilizing
local weather data, a critical aspect of farming, remains
a major challenge in developing regions due to limited
coverage. Traditional weather monitoring equipment is
large and capital-intensive, but the IoT is now allowing
18 Evan Thomas; Kay Mattson 2014. Instrumented Monitoring with Traditional
Public Health Evaluation Methods. Available at: http://www.mercycorps.org/sites/
default/files/Instrumented%20Monitoring%20Indonesia.pdf
19 http://www.economist.com/news/middle-east-and-africa/21665005-small-
farmers-africa-need-produce-more-happily-easier-it
32

for micro-weather stations to be deployed and utilized
for a range of activities, including the dissemination of
information to farmers on nutrient requirements, the
prediction of weather patterns, and the provision of
inputs into localized crop insurance schemes.
For example, Syngenta’s Kilimo Salama (“Safe
Farming”) project is a connected weather station that
monitors agricultural events and facilitates linkages
with insurance firms. The aim is to mitigate the risks
associated with adverse weather, thereby providing a
much-needed safety net for farmers while promoting
agricultural investment and improved livelihoods.
Safaricom’s M-Pesa mobile banking system assists
Kilimo Salama in keeping index insurance premiums
more affordable, helping transform smallholder farmers
into a commercially viable market segment for insurance
firms.20
Various types of micro-weather stations capture a range
of data such as air and soil temperatures (o
C and o
F),
air and soil moisture levels (%), solar radiation (W/m2),
wind direction, wind speed (m/s), atmospheric pressure
(hPa), amount of rainfall (mm), soil electrical conductivity
(EC, 0-23dS/m), and visual appearance (image capture).
At the end of each growing season, weather statistics
collected from solar-powered weather stations are
automatically compared with an index of historical
weather data. Rainfall measurements are factored into
specialized agronomic models to determine the impact
and likely loss that farmers experience. Insurance
payouts are then calculated and sent to the insured
farmers via automated mobile payments. This mechanism
has effectively automated and simplified the claims
process, cultivating a financially supportive environment
for individual farmers and encouraging agricultural
production at all levels.
In India, Nano Ganesh is a low-cost solution to provide
small-shareholder farmers with a tool that can remotely
control their micro irrigation pumps.21
Across the country,
20 “The Broadband Effect: Enhancing Market-based Solutions for the Base
of the Pyramid.” Available at: https://publications.iadb.org/bitstream/han-
dle/11319/6642/Opportunities_for_the_Majority_Report_The_Broadband_Ef-
fect.pdf.pdf?sequence=1
21 Food and Agriculture Organization of the United Nations, “Success Stories
on Information and Communication Technologies for Agriculture and Rural
Development.“ Available at: http://www.fao.org/3/a-i4622e.pdf
about 25 million water pumps are in use for farm
irrigation. Many of these pumps have to be manually
operated, based on rainwater conditions, electricity
availability and crop needs. For the average small-scale
farmer, the variability of these factors on a day-to-day
basis adds extra burdens in terms of time, labor and
fuel costs. In many cases, farmers need to travel long
distances through difficult conditions to access their
pumps from their households.
The Nano Ganesh unit works by attaching to the irrigation
pump, and serving as an actuator which can turn the
pump on and off via basic commands from a farmer’s
simple feature phone (2G mobile telephones). The
farmer is also able to check the availability of electricity
at the pump, as well as the availability of water near the
pump (with an additional water sensor). By August 2014,
around twenty thousand farmers in India had benefitted
from Nano Ganesh.
China is making great strides in applying IoT technologies
to improve agricultural production. Various information-
based applications have been developed, including
greenhouse remote monitoring, automatic drip irrigation,
and milk source safety information management to
enhance agricultural production. In Xinjiang, the “mobile
Internet of Things for Agriculture” project uses wireless
monitoring of agricultural greenhouses. Wireless water-
saving drip irrigation has also been used since 2011 to
monitor water quality and to save water in fresh water
aquaculture.22
Additional examples include the use of RFID tags for
monitoring livestock, which allows for more personalized
care for individual animals. In tea plantations in Sri
Lanka and Rwanda, WSNs are being utilized to monitor
soil moisture, as well as carbon, nitrogen, potassium,
calcium, magnesium, and pH levels. The sensors and
connectivity modules are powered through solar panels,
and the data are transmitted wirelessly.23
22 http://www.chinamobileltd.com/en/ir/reports/ar2010/sd2010.pdf
http://newsroom.hwtrek.com/?p=626
23 Minuri Rajapaksa “IoT for Productive Tea Plantation.” Available at:
http://wireless.ictp.it/school_2015/presentations/CaseStudies/IoTforTeaPlanta-
tion-SriLanka-Minuri.pdf
http://wireless.ictp.it/rwanda_2015/
http://www.ictp.it/about-ictp/media-centre/news/2015/6/teatime-with-iot.aspx
33

Resiliency, Climate Change,
and Pollution Mitigation
Following the 2004 Indian Ocean tsunami that devastated
coastal areas in India, Sri Lanka, Thailand and Indonesia,
the international community united to establish the
Indian Ocean Tsunami Warning System whereby kinetic
sensors (measuring waves and water flow) placed on
the ocean floor communicate data on potential tsunamis
to disk buoys floating on the ocean surface via acoustic
telemetry. The buoys then upload the information to
government authorities via satellite connectivity.24
The
ITU has launched a project to detect earthquakes and
seismic events via a network of sensors hosted on
submarine cables.
Other applications designed to promote resiliency include
a Red Cross project to explore the widespread installation
of connected alarm systems across high density urban
slums to quickly notify residents of fast-moving fires.
Fires can move quickly in informal settlements and slum
areas, given that homes are close in proximity. Faulty
wiring and indoor open hearths, when combined with the
density of these settlements, make combating fires quite
difficult, and, unfortunately, more likely to start. The Red
Cross is exploring the development of low-cost, solar-
powered sensors networked together to quickly detect
and relay to authorities when fires emerge. The network
sounds alarms, communicates to threatened residents
(via SMS and other modalities), and its connected
sensors identify via GPS where the fire has started,
notifying authorities of the location where fire mitigation
efforts should be targeted.25
Currently, the intervention is
being tested in Nairobi and Cape Town, with participation
by two thousand households.
Many analyses are now trying to model risks and
vulnerabilities in the wake of climate change. For
example, climate models and disaster risk models
can now be combined with satellite imagery of human
settlement (such as night-time lights) to estimate
24 http://www.kophuket.com/phuket/homemenu/tsunami.html and https://
en.wikipedia.org/wiki/Indian_Ocean_Tsunami_Warning_System
25 https://drive.google.com/file/d/0B1vf6TLGIC0yZk9UU2t2UGFmNlE/
view?usp=sharing
economic exposure to risk.26
New sensor data also
include unmanned aerial vehicles (“drones”) and spatially
referenced (geo-referenced) video.
Geo-referenced video has been used to quickly identify
sites of standing sewage and water to aid in cholera
risk mapping in Haiti 27
and the vulnerability of homes
in Los Angeles, to wildfire.28
Drones can provide very
high-resolution (VHR) satellite imagery in 2-D and 3-D,
which can be useful in mapping complex urban riverine
topography, and which has been used in Haiti for flood
26 Ceola, S., Laio, F., Montanari, A. (2014). Satellite night-time lights
reveal increasing human exposure to floods worldwide, 7184–7190.
doi:10.1002/2014GL061859. Received; and Christenson, E., Elliott,
M., Banerjee, O., Hamrick, L., Bartram, J. (2014). Climate-related hazards:
a method for global assessment of urban and rural population exposure to
cyclones, droughts, and floods. International Journal of Environmental Research
and Public Health, 11(2), 2169–92. doi:10.3390/ijerph110202169
27 Blackburn, J. K., Diamond, U., Kracalik, I. T., Widmer, J., Brown, W., Mor-
rissey, B. D., … Morris, J. G. (2014). Household-Level Spatiotemporal Patterns
of Incidence of Cholera, Haiti, 2011. Emerging Infectious Diseases, 20(9),
1516–1520.
28 Burkett, B., Curtis, A. (2011). Classifying Wildfire Risk at the Building
Scale in the Wildland-Urban Interface: Applying Spatial Video Approach-
es to Los Angeles County. Risk, Hazards Crisis in Public Policy, 2(4), 1–20.
doi:10.2202/1944-4079.1093
Photo credit: Marco Zennaro. Calibration of a sensor node
measuring weather parameters (temperatures, pressure,
humidity, light) in Nairobi, Kenya.
34

modelling, as well as in Nepal after the destructive 2015
earthquake.29
New datasets can help in understanding vulnerability
and mobility, and data to estimate mobility patterns can
be gleaned for example from geo-located tweets. One
researcher analyzed New York City tweeters before,
during, and after Superstorm Sandy to show that pre-
disaster mobility patterns can indicate the potential range
of mobility during a disaster.30
Other indicators of mobility
include transit data by bikes,31
buses and subways being
made available by hundreds of municipalities.32
Transit
data can monitor population flux at different times of day,
and provides just one example of open data which cities
are releasing that could be valuable for risk assessment.
Population movement analyses based on Call Detail
Records (CDRs) from mobile network operators have
also been used for planning malaria elimination strategies
and, more recently, to monitor population movements in
the 2014 Ebola outbreak in Guinea, Sierra Leone, Liberia,
Nigeria and Senegal. Despite some initial challenges,
epidemiological models of the spatial spread of Ebola were
developed to model the spread of the virus, and predict its
possible development (Figure 11, page 30). These models
can help assess the likely routes of infected individuals
between populations, predict possible new outbreaks and
help focus the delivery of eventual vaccines. However,
challenges remain in terms of establishing the processes
by which such data can be shared and released in a timely
fashion.33
29 Big Data in the Disaster Cycle: Overview of use of big data and satellite im-
aging in monitoring risk and impact of disasters; Tellman, Schwarz, Burns, Adams
30 Wang, Q., Taylor, J. E. (2014). Quantifying Human Mobility Perturbation
and Resilience in Natural Disasters. arXiv.org, physics.so(11), 1987. doi:10.1371/
journal.pone.0112608
31 Zaltz Austwick, M., O’Brien, O., Strano, E., Viana, M. (2013). The struc-
ture of spatial networks and communities in bicycle sharing systems. PloS One,
8(9), e74685. doi:10.1371/journal.pone.0074685
32 https://code.google.com/p/googletransitdatafeed/wiki/PublicFeeds for a list
33 http://www.economist.com/news/science-and-technology/21627557-mo-
bile-phone-records-would-help-combat-ebola-epidemic-getting-look
Additionally, privacy issues still need to be addressed,
such as the right level of anonymity (and aggregation,
such as from the individual to a group level) for records to
ensure an appropriate balance is struck between ensuring
individual privacy, while preserving the value of the data
for social aims (including crisis response and policy
planning).34
Urban air pollution is a major problem in many developing
country cities. The World Health Organization (WHO)
suggests that polluted air contributes to one in eight
deaths worldwide, as dirty air causes lung damage, heart
disease, strokes and cancer. The WHO also estimates
that indoor air pollution in homes in Africa contributed to
nearly 600,000 deaths in 2012.35
To measure the extent
of the problem, air quality sensors are being deployed in
a range of cities to track levels and changes in pollutants.
One such project, “Fresh Air in Benin“ is focused on
developing a network of air quality sensors to capture
and send data every 20 minutes via GSM connectivity.36
Water flow sensors are also being used to help collect
hydrological data in developing countries where local
data on river flow and levels may not be regularly
collected. These sensors can also provide early warning
of floods. For example, the Hidrosónico is a water stream
gauge that uses a sonar range sensor to measure the
distance to water surface level. The module sends the
readings on a regular basis to recipients (via SMS or
email) or to a cloud application. The unit is also equipped
with a rain gauge to monitor precipitation, and is currently
deployed in Honduras.37
34 http://www.unglobalpulse.org/projects/mobile-data-privacy
35 http://www.nytimes.com/2014/04/17/business/energy-environment/mea-
suring-africas-air-pollution.html
36 http://www.worldbank.org/content/dam/Worldbank/Feature%20Story/Mak-
ers4development.FV%20USAID.pdf
37 http://dai.com/our-work/solutions/dai-maker-lab and https://github.com/
DAI-Maker-Lab/hidrosonico
35

Natural Resource Management
Protecting land and the environment from pollution
and illegal logging, as well as protecting natural wildlife
from poaching, are major natural resource challenges
in developing countries. One example of a working
prototype drone has been developed to help monitor
wildlife in remote and mountainous areas in the United
Arab Emirates (UAE). Camera traps take automatic
photos of animals, triggered by motion. However,
reaching the camera traps is dangerous and costly and it
is time-consuming to upload the photos, costing at least
Dh1 million a year to capture this data. To combat these
challenges, a team from New York University in Abu
Dhabi has developed a 2.2kg drone that can fly for 1.5
hours for up to 40 km, collecting different types of data
including images, salinity and atmospheric data in the
mountainous Wadi Wurayah National Park.38
In the camera trap, the transmission system uses
low-power XBee communications run on solar power,
which turns on at regular 2 to 3 minute intervals to check
whether the drone is approaching. The system also has a
back-up transmission system via Wi-Fi in case of failure.
The drone hosts the same XBee and Wi-Fi systems and
a microprocessor that is permanently turned on and
powered by a litho-battery, which can be recharged from
solar power once the drone returns to base. The drones
are currently used to assess and evaluate the animal
population, rather than climate change per se. However,
new applications could include monitoring water quality
and salinity in coastal regions and desalination of the
coast.
Illegal poaching of large wildlife is a major concern across
sub-Saharan Africa. For example, 40,000 elephants
were killed illegally in 2014, primarily for their tusks, while
demand for the horns of the black rhinoceros has lead to
a 96% decline in the species’ population from 1970 to
1992, with fewer than 20,000 animals remaining. Every
eight hours, a rhinoceros is killed in southern Africa.39
To combat poaching operations, a wide number of
projects are utilizing emerging IoT technologies such as
38 http://gulfnews.com/news/uae/government/uae-drones-for-good-award-
wadi-drone-makes-conservationists-job-easier-1.1452917
39 http://airshepherd.org/
connected drones for surveillance as well as long-range
wide area technologies to track wildlife and monitor
activity at the boundaries of game parks.40
In Timor-Leste, the National Directorate of Fisheries and
Aquaculture (NDFA) is working in partnership with the
FAO-Spain Regional Fisheries Livelihoods Programme
for South and Southeast Asia (RFLP) to introduce a
programme using radio location beacons to protect local
waters from illegal fishing and to provide emergency
response to fishermen in distress. The partnership
introduced low-cost personal location beacons (PLBs)
as part of a community-based system for identifying
illegal, unreported and unregulated fishing. When local
fishermen identified illegal fishing boats in the Timor-
Leste waters, the location beacon is activated and data
is are sent to an Internet-enabled mapping platform used
by authorities to track and apprehend illegal fishing boats,
or initiate rescue services.41
Acoustic sensors have also been developed to monitor
wild populations of seabirds. For example, Nexleaf has
worked with the US Fish Wildlife Service and the
Coastal Conservation Action Lab to deploy four sensors
on Tern Island from December 2011 to May 2012.42
The deployment consisted of four wildlife acoustic
monitors and two Wi-Fi radio repeaters powered by 20W
solar panels, and one gateway computer. The project
demonstrated the performance capabilities of satellite-
based WSNs for long-term monitoring of seabird
colonies.
The United States Forest Service (USFS) has developed
a project to study the state of urban and rural forest
ecosystems in real-time to keep them healthy and
more sustainable.43
Climate change will also be better
understood, and lessons can be learned in order to take
advantage of the effects of climate change. USFS has
monitored wildlife across the US for decades, manually
collecting information from air and soil temperature to
40 http://airshepherd.org/ and http://gblogs.cisco.com/uki/week-4-how-can-
technology-help-the-anti-poaching-activities/
41 Food and Agriculture Organization of the United Nations, “Success Stories
on Information and Communication Technologies for Agriculture and Rural
Development”, available at: http://www.fao.org/3/a-i4622e.pdf
42 http://nexleaf.org/project/tern-island
43 http://smartforests.org/
36

solar radiation. The IoT is helping to automate some of
the data collection activities.
Now, the USFS has developed a sensor-based system to
obtain real-time measurements and combine these with
traditional field studies and long-term records of patterns
and processes to analyze environmental changes. Some
sensors reach around trees to measure their growth,
others use motion-triggered webcams and sensors to
capture pictures of wildlife 24/7 and track the presence
(or absence) of endangered species. Sensors detect
and deliver high-resolution data wirelessly or through
a cellular network to a central web portal. Similarly,
Rainforest Connection is using microphone sensors in
smartphones to monitor for sounds associated with illegal
logging (from chainsaws, trucks and motorcycles).44
There are also other interesting developments in relation
to commercial crops, often a vital source of income in
rural communities. Over the last two decades, the red
palm weevil has become a growing threat to palm trees
in many parts of the world.45
Early detection is difficult,
since many palm trees may not show visual evidence
of infection until it is too late for trees to recover. A
prototype bioacoustic sensor has been developed to
detect sounds of larvae activity, with wireless reporting
to a control station to check the status and evolution of
palm tree orchards.
Energy
Another interesting IoT application in the energy sector
in developing countries has been the rapid adoption of
off-grid solar panel systems that provide steady electrical
power to low-income families. As Figure 8 (page 26)
highlights, electricity is used by only 58% of the urban
population and by only 12% of the rural population in
sub-Saharan Africa. Challenges of grid availability, cost of
service and frequent service interruptions plague on-grid
electricity users across much of the developing world.
M-Kopa in Kenya exemplifies this new technology, which
is comprised of photovoltaic cells and a battery system
44 Interview with Dave Grenell, Rainforest Connection, on May 19, 2015.
https://rfcx.org/
45 www.mdpi.com/1424-8220/13/2/1706/pdf
and communications model.46
Individuals purchase
the system at a discounted rate with the capital costs
amortized over an initial purchase period. After installation
in their dwelling, the customers are then able to utilize
the electricity generated by the solar cells to power home
appliances. They must make regular payments (usually
via mobile money systems, e.g. M-Pesa or Airtel Money)
in order to continue using the device. M-Kopa is able
to remotely monitor the amount of electricity captured/
stored and whether the device is working appropriately
through the device’s connectivity module.
Across the developing world, wood and charcoal
burning cook stoves are used extensively to prepare
meals and provide a source of heat inside homes. The
resulting indoor air pollution contributes to approximately
4 million deaths a year, out of the 3 billion people
worldwide who utilize biomass to prepare their meals.
As a result, a number of initiatives are in place to help
lower income households use less polluting methods
for meal preparation and heating in the way of improved
cookstove projects. For example, the US Government
has supported a five-year “Global Alliance for Clean
Cookstoves” which aims to achieve a goal of enabling
100 million homes to adopt clean and efficient cooking
solutions by 2020.47
IoT sensors are playing a role in this
initiative by helping measure the black carbon emitted
by cookstoves in real-time, as well as monitoring and
evaluating projects to disseminate improved cookstoves.
In one particular project in Sudan, the use of improved
cookstoves via sensor-enabled recording instruments
was compared to traditional survey data (captured by
an enumerator) to determine whether the latter method
accurately reflected usage captured by sensors. In this
case, the study found that survey participants were over-
reporting daily cooking hours by 1.2 hours on average,
and daily cooking events by 1.3 events. The results
suggest that survey-based methods of evaluation may
be misstating the actual impact and usage of the newer
technologies.48
46 www.gsma.com/mobilefordevelopment/wp-content/uploads/2013/01/Sus-
tainable-Energy-and-Water-Access-through-M2M-Connectivity.pdf
47 http://www.state.gov/r/pa/prs/ps/2015/09/247240.htm
48 “Comparing Cookstove Usage Measured with Sensors Versus Cell Phone-
Based Surveys in Darfu, Sudan,” by Daniel Lawrence Wilson et al. Chapter 20 of
“Technologies for Development: What is Essential.”
37

IoT technologies can be used to produce more accurate
data, especially when used in conjunction with subjective
perceptions recorded via survey response.
Other Sectors
Examples of IoT deployment impacting other facets
of global development abound, and the opportunities
to improve service delivery, and other aspects of
development work, are limited only by human creativity
and the resources available. Table 3 below highlights
some of the various IoT interventions as they map to
the Millennium Development Goals (MDGs) and the new
Sustainable Development Goals (SDGs) as adopted by
the United Nations.
38

Table 3 – Examples of IoT interventions mapped to the Millennium Development
Goals (MDGs) and Sustainable Development Goals (SDGs)
Sector MDG SDG Examples
Health, Water
Sanitation
MDG 4:
Child Health
MDG 5:
Maternal health
MDG 6:
Combat HIV/
AIDS, malaria and
other diseases
SDG 3:
Ensure healthy lives and
promote well-being for all at all
ages.
SDG 6:
Ensure availability and
sustainable management of water
and sanitation for all.
Sensor- and SMS-enabled village water pumps
(Rwanda, Kenya); GSM-connected refrigeration for
vaccine delivery in the ‘cold chain’ (Global); sensor-
enabled ‘band aid’ to monitor Ebola patients’ ECG,
heart rate, oxygen saturation, body temperature,
respiratory rate and position, all remotely (West
Africa); water stream gauge with sonar range sensor
to monitor river flow and depth (Honduras); water
flow sensors and motion detectors in latrines to
monitor efficacy of hygiene training and intervention
(Indonesia).
Agriculture
Livelihoods
MDG 1:
End Poverty
Hunger
SDG 1:
End poverty in all its forms
everywhere.
SDG 8:
Promote sustained, inclusive and
sustainable economic growth, full
and productive employment and
decent work for all.
SDG 2:
End hunger, achieve food security
and improve nutrition, and
promote sustainable agriculture.
Connected micro-weather stations improving
localized weather data and provision of crop failure
insurance (Kenya); low-cost mobile-controlled micro
irrigation pumps (India); soil-monitoring sensors
used to improve tea plantation production (Sri
Lanka, Rwanda); RFID-based food supply testing
and tracking system (India) and RFID based livestock
programmes for tracking, theft prevention and
vaccination records (Botswana, Senegal and
Namibia).
Education
MDG 2:
Universal
Education
SDG 4:
Ensure inclusive and equitable
quality education and promote
lifelong learning opportunities for
all.
Smart identity cards with biometric features for all
public school students to improve service delivery
(Nigeria); biometric clocking device to improve
teacher attendance in real-time (South Africa).
Environment
Conservation
MDG 7:
Environment
SDG 12:
Ensure sustainable
consumption and production
patterns.
SDG 13:
Take urgent action to
combat climate change and its
impacts.
SDG 14:
Conserve and sustainably use
the oceans, seas and marine
resources for sustainable
development.
SDG 15: Protect, restore
and promote sustainable use
of terrestrial ecosystems,
sustainably manage forests,
combat desertification, halt and
reverse land degradation, and halt
biodiversity loss.
Radio-based cloud-connected devices to
identify and track the presence of illegal fishermen
(Timor-Leste); air pollution sensors to monitor urban
outdoor air pollution (Benin); acoustic sensors to
monitor sea bird populations (global); sensors and
connectivity to protect game park perimeters and
track animals (Africa); connected unmanned aerial
vehicles monitor national parks and connecting
images from camera traps (UAE); acoustic sensors
in tropical rainforests ‘listening’ for illegal logging
(Indonesia).
39

Sector MDG SDG Examples
Resiliency,
Infrastructure
and Energy
SDG 7:
Ensure access to
affordable, reliable,
sustainable and modern
energy for all.
SDG 9:
Build resilient
infrastructure, promote
inclusive and sustainable
industrialization, and foster
innovation.
SDG 11:
Make cities and human
settlements inclusive, safe,
resilient and sustainable.
Networked fire/smoke alarms in high-density urban slums/
informal settlements (Kenya, South Africa); Connected
buoys as part of the tsunami monitoring system (Indian
Ocean); off-grid micro solar electricity systems for
electricity for lower-income households (east Africa, India);
connected black carbon- and use sensors to monitor cook
stoves (Sudan); sensor-connected matatus (mini-buses)
tracking velocity, acceleration, and braking to curb danger-
ous operation of public transportation (Kenya).
Governance
Human Rights
SDG 10:
Reduce inequality within
and among countries.
SDG 16:
Promote peaceful and
inclusive societies for
sustainable development,
provide access to justice
for all and build effective,
accountable and inclusive
institutions at all levels.
Retinal scans used for ATMs providing secure biometric
cash assistance to displaced refugees (Jordan).
Cross-Cutting
MDG 3:
Gender Equality
MDG 8:
Partnership
SDG 5:
Achieve gender equality
and empower all women
and girls.
SDG 17:
Strengthen the means of
implementation and
revitalize the global
partnership for sustainable
development.
Table 3 – Examples of IoT interventions mapped to the Millenium Development
Goals (MDGs) and Sustainable Development Goals (SDGs) (continued)
40

Challenges to the
deployment, scale
and impact of the IoT
in developing countries
41

Challenges of the Internet of Things
Despite all the exciting possibilities brought about by
the IoT and Big Data, significant challenges persist . The
same infrastructure that enables people to create, store
and share information may also jeopardize their privacy
and security . These same techniques can be used for
large-scale and targeted surveillance . Abuse of these
techniques could turn the ‘Information Society’ into the
‘Surveillance Society’, as identity management systems
improve without parallel emphasis on anonymity and
ownership of personal data .
Society’s most advanced systems and infrastructures are
now so complex that some of them are becoming hard
to manage effectively . Where they are designed wisely
and used effectively, policy and regulatory frameworks
can help the development of the IoT . However, outdated
or poorly designed frameworks can prove a hindrance
and obstacle to the further growth of the IoT . While many
parts of daily life become more connected, some remain
woefully underconnected . Conversely, other elements
of an individual’s daily life may be overwhelmed as the
explosion of new devices will require new infrastructure
and technologies .
Technological and human capabilities are often
insufficient in developing countries . Financial support may
be lacking . There are often not enough technically literate
people with IT skills in local areas who are capable of
implementing the use of sensors or other devices into
their daily lives . Figure 12 summarizes some of the
emerging challenges in relation to the IoT and data .
Figure 12: Summary of Emerging Challenges in relation to the IoT
Source: Pepper, R . Garrity, J . (2014) The Internet of Everything: How the Network Unleashes the Benefits of Big Data .
Global IT Report 2014 . WEF . http://www3 .weforum .org/docs/GITR/2014/GITR_Chapter1 .2_2014 .pdf
Technical Policy
Legacy
Regulatory
Models
Data
LocalizationReliability
Scaling
Power
Security
Privacy
Spectrum
Bandwidth
Constraints
Standards
Interoperability
IPR
Connectivity
Cost
Capacity
IPv6
Access to data/
Open data
Cross -
border traffic
Governance
42

Technical Challenges
Reliability is a concern with regard to the durability of
devices to withstand external conditions. Sensors, too,
need to be calibrated to ensure proper measurements.
In terms of scalability, the way in which resources are
scaled to match growth in the IoT may matter. Data
centres, for example, are constantly being redesigned
in terms of electrical power, cooling resources, and
space design to advance current capabilities. However,
the connectivity requirements of billions, as opposed
to millions, of connected objects impose very different
demands on data centres. As the IoT scales up and
expands from billions into tens of billions of connected
devices, IP networks have to be able to manage the huge
scale of device connectivity.
Power requirements vary greatly, with higher bandwidth
devices requiring much more power. Connectivity
challenges were discussed earlier, and include limited
data network coverage. According to Laura Hosman
of Inveneo, the top five hardware challenges in the
application of ICTs in development are: electricity/
power/energy; cost; environment; connectivity; and
maintenance and support.1
The costs associated with
the sensors, connectivity modules and the connectivity
service can still prove prohibitive for many interventions
(such as for individual small shareholding farmers).
Organizations are starting to explore shared models of
sensor module ownership such as community ownership,
or ‘sensors as a service’.2
Inadequate human capacity may prove a major issue in
some locations. Small-scale organizations may not be
trained properly to use the technology. There may also
be underlying issues that inhibit training. For instance, if
80% of the target population is illiterate, is SMS text really
the best form of communication? There may also be an
inadequate number of trained people or technicians to
respond, once a system signals a problem. If it is difficult
to fix manual pumps on-the-ground, it may be difficult
to find the resources to fix more complicated, seemingly
1 http://www.inveneo.org/wp-content/uploads/2014/07/FINALTop-ICT-
Hardware-Challenges-White-Paper.pdf
2 http://www.slate.com/articles/technology/future_tense/2015/06/community_
drones_helps_indonesia_s_dayaks_protect_their_land.html
mysterious ‘black boxes’ without back-up strong
structures in place to deal with breakdowns.
Further challenges may arise from human behavior as a
limiting factor, with reluctance to adopt new technology
also a possible concern. People are often resistant and
reluctant to modify their behavior to fit with systems, and
prefer that systems adapt to meet their needs. Deloitte
cites the example of one electrical utility company which
installed smart meters in millions of homes in North
America, expecting that consumers would consult online
dashboards of monthly usage, and modify behavior
to save money and energy, benefit the environment.
Three years after the meters were deployed, only 6% of
households had viewed the dashboard at all, while fewer
than 2% had consulted the dashboard more than once.3
Indeed, according to some projects, human behavior may
prove a more significant barrier to adoption than some
technical challenges.
In the short-term, the transition to IPv6 has proved
challenging for some countries and some organizations to
date. However, practically speaking, IPv6 may even prove
a limiting factor in some M2M deployments, requiring
all partners in a project to have made the transition. For
example, partner universities first need to transition to IPv6
before they can implement a WSN project. Government
can help play a leadership role in the transition to IPv6, and
help aggregate and/or stimulate demand. The migration
from IPv4 to IPv6 should help resolve Internet address
issues in the long-term, but the short-term challenge of
providing adequate address space for billions of objects
will persist.
Policy Considerations
Data localization regulations and limits on cross border
traffic may impede the ability of managers to send data
to cloud-based servers where data may be analyzed.
While Open Data policies are increasing adopted, there
are examples of governments clamping down on access
to data collected and generated by sensors.4
The IoT has regulatory implications across a set of areas,
3 Deloitte Tech, Media Telecoms (TMT) Predictions 2015.
4 www.washingtonpost.com/blogs/monkey-cage/wp/2015/04/08/five-charts-
that-may-soon-be-illegal-in-tanzania/
43

including licensing, spectrum management, standards,
competition, security and privacy — only some of which
are the familiar territory of telecom regulators, compared
with other areas where different regulators may typically
take the lead. The ‘full’ IoT or Internet of Everything
(IoE) is likely to require more ‘joined up’ regulation,
with telecom/ICT regulators working more closely with
their counterparts in data protection and competition,
but also with emergency services, health and highway
authorities, as legacy regulatory models (e.g. power utility
regulations) may prove inadequate to deal with emerging
technologies (e.g. ‘smart grid’ technologies).
Similarly, broader governance issues may impede the
adoption of the IoT, such as in the case of the slow
adoption of connected thermometers to protect the
vaccine cold chain due to challenges in certification
approval for new technologies in the World Health
Organization’s Pre-Qualified Systems (PQS).
Overlapping Issues
The proliferation of, and growth in, new IoT technologies
are built mainly on interoperability. In order for a car,
airplane, parking meter or pill bottle to send and receive
important data, it needs to be able to connect to
other systems and networks seamlessly and securely.
Interoperability is the ability to transfer useful data and
other information across systems, applications, or
components in four broad layers — technological; data;
human; and institutional.5
According to one source,
there are at least 115 different protocols used by IoT
devices to connect to the cloud today.6
A recent paper
by McKinsey notes that “interoperability is required
to unlock more than US$4 trillion per year in potential
economic impact for IoT use in 2025, out of a total
impact of US$11.1 trillion. On average, interoperability is
necessary to create 40% of the potential value that can
be generated by the IoT in various settings.”7
Although various approaches exist to help promote
interoperability, standards are one collaborative approach
5 GSR Interoperability Discussion Paper by Urs Gasser, Berkman Center.
6 NetHope SDG ICT Playbook. Available at: http://solutionscenter.nethope.org/
assets/collaterals/NetHope_SDG_ICT_Playbook_Final.pdf
7 Manyika, James, et. al., The Internet of Things: Mapping the Value beyond the
Hype. McKinsey Global Institute, June 2015. p. 2.
to interoperability, and can achieve high levels
of interoperability.8
Privacy and security are often cited as two of the most
significant (and closely related) issues in large-scale
IoT deployment. Privacy, security and anonymity are
all related, but separate, concepts. Privacy (related to
confidentiality) is the ability to define the intended target
audience for data. Anonymity is the quality or state
of being unknown to most people. A secure system
is a system free from weakness or vulnerability. For
example, electronic health files and car license plates
are private (and confidential), but not anonymous.
A breach of security may or may not result in a loss
of privacy, depending on the data downloaded and
how it is subsequently used. While ICTs do provide
greater opportunities for communication and income
related activities for lower-income populations, careful
consideration is needed of the risks associated (loss of
privacy, etc.).
Without adequate security, intruders can break into IoT
systems and networks, accessing potentially sensitive
personal information about users, and using vulnerable
devices to attack local networks and devices, thereby
breaching their privacy. This is a particular issue when
devices are used in private spaces, such as in individuals’
homes. Operators of IoT systems, as well as others with
authorized access to the data produced, are likely to be
in a position to “collect, analyze, and act on data from
within previously private spaces”.9
Privacy concerns may still arise, even where systems are
secure and functioning as intended. For example, does
sponsorship of projects by third parties entitle them to
access the resulting data? Third parties almost invariably
respond that the data are high-level and aggregated,
making it anonymous, or that they are using the data for
beneficial purposes that outweigh any loss in privacy.
Another issue related to privacy and anonymity is the
address space of connected devices. Identifiers used in
one network need to be understandable and/or usable
(i.e. interoperable) in another network. In the Internet of
8 GSR Interoperability Discussion Paper by Urs Gasser, Berkman Center.
9 GSR Discussion Paper on IoT by Prof. Ian Brown, Oxford Internet Institute..
44

Things, consumers will likely want to use different objects
across multiple kinds of heterogeneous networks, which
will need the identities of things to be “federated” or
capable of being translated accurately and recognized
by different networks. The IoT will contain billions of
objects that must be uniquely identified, a challenge for
which there is currently so far no internationally-agreed
solution, although Internet Protocol version 6 (IPv6) may
eventually become the default solution.
On a more practical level, lock-in to mobile service
providers is a real likelihood for many projects. Firms
operating large networks of IoT devices via mobile
telephony networks using a fixed SIM in each device
may not find it easy to switch network at the end of a
contract, or where there is device roaming in different
network areas.
It could also be very difficult to renegotiate the terms of
the mobile contract, or swap service temporarily to take
advantage of better service from a different provider,
raising concerns of anti-competitive behaviour.
Spectrum and bandwidth requirements may impede
the adoption of IoT devices and services. According
to Cisco’s 2015 Visual Networking Index study, over
10 billion new devices will come online between 2014
and 2019, and total global IP traffic is growing at 23%
Compound Annual Growth Rate (CAGR).10
While the
sheer diversity of IoT devices will result in a wide range
of network requirements, the aggregate impact will lead
to increasing demands for wireless spectrum to support
wireless data transmission. A flexible and sufficient
spectrum bandwidth regime will be necessary to ensure
innovation and adoption are not stifled.
10 Cisco Visual Networking Index 2015, at http://www.cisco.com/c/en/us/solu-
tions/service-provider/visual-networking-index-vni/index.html
Photo credit: Marco Zennaro. Engineer with a GSM-enabled soil moisture sensor node in a tea factory plantation in Rwanda.
45

Recommendations
46

A number of enabling policies can be put in place to
facilitate the deployment of the Internet of Things, as well
as Big Data. As explained previously, policies to support
rapid and effective adoption of the IoT need to involve
a range of stakeholders to help promote successful
deployments. At the national level, policies can be put in
place to support and facilitate the fast development of
IoT, as well as eliminate the barriers and challenges to
be overcome (described in the last section). Some key
aspects to be considered include:
1. Create a policy framework and master plan
addressing the IoT – as the IoT is likely to become a
huge sector on its own right in a number of countries,
countries wishing to take advantage of its benefit
should develop a master plan and/or additional
funding for broadband including consideration of
the IoT to accelerate the growth of the IoT and to
capitalize on these benefits. In the UK for instance,
Prime Minister David Cameron announced [in March
2014] an additional GBP 45 million funding for the
development of IoT.1
Malaysia’s Ministry of Science,
Technology and Innovation and its applied research
agency released a National Internet of Things (IoT)
Strategic Roadmap in mid-2015.2
Independently,
some donor partners in the ICT4D community have
also established “Principles for Digital Development”
that aim to facilitate the adoption of the IoT in
development.3
Governments and policy-makers
should work closely with industry to understand the
issues involved. This policy should also consider
how new IoT systems can interface with pre-existing
legacy infrastructure to protect and make full use of
existing investments in infrastructure.
2. Give consideration to sourcing any additional
spectrum which may be needed for IoT. As
discussed in Section 2, connectivity and therefore
spectrum is a key part of supporting the expansion of
1 “‘Internet of things’ to get £45m funding boost”, 9 March 2014,
available at: http://www.bbc.com/news/business-26504696
2 https://www.telegeography.com/products/commsupdate/arti-
cles/2015/07/14/malaysia-publishes-national-roadmap-for-iot/?utm_
source=CommsUpdateutm_campaign=9add5560b9-CommsUp-
date+14+July+2015utm_medium=emailutm_term=0_0688983330-
9add5560b9-11619241
3 http://digitalprinciples.org/
the IoT. Efficient and effective spectrum management
is therefore key for ensuring efficient connectivity.
In December 2015, Australian authorities freed up
additional spectrum bands dedicated to the use of
IoT and M2M.
3. Promote and support a broad, vibrant ecosystem
for IoT, including support for tech start-ups and
incubators. This includes promoting policies to
facilitate innovation and development and eliminate
policies that restrict or prevent innovation (such as
restrictions to the free movement of data or the
ability to trade in digital services).
4. Promote Data Centres — Depending on the
centralized or decentralized nature of the IoT
deployments in a country, it may be helpful to launch
more local data centres, supported by reliable and
quality electricity, tailored tax incentives, and low or
more flexible labour costs.
5. Promote standards that facilitate interoperability
across the IoT ecosystem, foster investment,
competition and scale to enable cost-effective
solutions.
6. Trust and confidence in the IoT are fundamental and
must be designed into the IoT from the outset. Two
key components to ensure trust and confidence
are privacy and security: a) Strategies to protect
privacy must take a range of risks into account from
a variety of different sources as well as adapt to local
regulations; and b) Accelerate research into IoT-
related security threats to minimize the downsides
of the IoT across M2M and M2P communications.
The exponential growth of increased attack vectors
(in terms of type of data generated and the variety
of things and devices connected to the Internet) may
give attackers easy ways to access networked data.
This report has made the case for the use of IoT to
improve people’s lives around the world. It has outlined
a number of practical and technical considerations in
the deployment of IoT systems in the hope of improving
outcomes in development projects and accelerating
the adoption of IoT in developing countries to improve
people’s lives.
47

Annexes
48

Annex 1: Projects By Sector
This Annex presents a glossary of additional information
about projects mentioned in this report.
Health
Nexleaf Analytics — Cold Chain Monitoring
Nexleaf Analytics is a non-profit technology company
that builds wirelessly connected devices and sensor
technologies for critical public health and environmental
interventions. Nexleaf’s Cold Trace remote temperature
monitor uses mobile phones to collect and wirelessly
transmit temperature data from refrigerated units that
store vaccines. Sensors wirelessly upload temperature
data and the system generates SMS and email alerts
when vaccine refridgeration equipment registers
dangerous temperatures. By providing real-time
information on equipment performance, vaccine supply
chains become more secure. Fridge performance
information by model and power availability data by
location can improve forecasting and capacity planning
in the future.
http://nexleaf.org/technology/cold-chain-monitor
Smart Connect – Cold Chain Monitoring
Smart Connect is a “communication appliance”
developed by PATH and Inveneo that uses SMS to
improve the reliability and performance of one of
the most important systems in all of global health:
the medical “cold chain”. With over 200,000 vaccine
refrigerators in use in the developing world alone, most
of them in harsh and remote environments, keeping
the cold chain up and running is a major challenge that
Smart Connect seeks to overcome. The device was
developed to confront communication barriers and
address the cold chain challenge by bringing a “digital
dial tone” to remote health posts in the developing
world. Representing a significant departure from the
traditional method of tracking and recording refrigerator
temperatures, Smart Connect now records the
temperature and sends out alert messages whenever
there is a problem. Messages are then sent to a
website and automatically relayed to service
technicians who can respond immediately. Additionally,
managers have access to daily summaries of the
refrigerators’ temperatures, which are posted online to
the Smart Connect website.
http://homes.cs.washington.edu/~eorourke/papers/smart_
connect_nsdr.pdf
STAMP2
The United States Agency for International
Development (USAID) has supported and employed IoT
solutions via connected wearable technologies, most
recently through its backing of the Sensor Technology
and Analytics to Monitor, Predict, and Protect Ebola
Patients program (STAMP2 for short), created by the
Scripps Translational Science Institute (STSI). This
intervention has been tested on Ebola patients in the
United States and is being scaled up to meet the
needs of other government agencies for the Ebola
treatment strategy in Liberia. STAMP2 collects patient
data, including ECG, heart rate, oxygen saturation,
body temperature, respiratory rate and position.
This data is then sent to a centralized platform so it
can be monitored and analyzed over a long period
of time to alert physicians to abnormal changes in a
patient’s behavior or health. The STAMP2 sensor will
be deployed in Ebola-stricken areas using a connected
health patch or “Smart Band-Aid”. The fully equipped
sensor-enabled band-aid is estimated to cost
approximately US$100 with a maximum battery life of
10 days – making it ideal for use in field-based Ebola
treatment centers. Deployment of the STAMP2 sensor
would improve the broader Ebola response initiative by
reducing emergency response times in critical areas
and enabling emergency responders to detect Ebola
patients earlier and to monitor them more efficiently.
http://mobihealthnews.com/40564/scripps-wins-usaid-grant-
to-monitor-ebola-patients-with-medical-wearables/
49

Water Sanitation
The Hidrosonico Project
Water flow sensors are being used to aid in the
collection of hydrological data in developing countries
where local data on river flow and levels may not
be regularly collected. These sensors can also help
with early warning for flooding. The Hidrosónico is a
water stream gauge that uses sonar range sensors
to measure the distance to water surface level.
The module sends the readings on regular basis to
recipients (via SMS or email) or to a cloud application.
The unit is also equipped with a rain gauge to monitor
precipitation, and is currently deployed in Honduras.
http://dai.com/our-work/solutions/dai-maker-lab;
https://github.com/DAI-Maker-Lab/hidrosonico
RW Siaga Plus+ Program
Between September 2009 and September 2011,
Mercy Corps in Indonesia conducted and evaluated
various approaches to their water and sanitation
programme RW Siaga Plus+, in the Indonesian
kelurahan (sub-district) of Margahayu, Bekasi, as well
as in sixteen poor urban neighborhoods in three other
kelurahan located throughout West Jakarta. A major
goal of this programme was the creation of healthy
physical environments in urban poor settlements
through increased access to clean water supplies and
improved sanitation. Using two different evaluation
methods, the organization and its partners measured
the programme’s effectiveness on achieving behavioral
change and overall programme targets, enabled by
data collected from water flow sensors deployed in the
field. In one programme delivering water, hygiene and
sanitation interventions, flow sensors, in combination
with motion detectors, amassed data that was used to
measure the impact of behavior change training, with
the intent of increasing hygienic actions (washing after
latrine use). The study found community participants
were washing hands after latrine usage; however, the
survey responses (those who indicated they wash
their hands after using the latrine) were significantly
higher than the data captured by the motion sensors,
suggesting over-reporting in the verbal and self-
reported surveys.
http://www.mercycorps.org/sites/default/files/Instrumented%20
Monitoring%20Indonesia.pdf
SmartPump (Mobile Enabled Transmitter) - Maintenance
Service Provider Model
The Smith School of Enterprise and the Environment
at Oxford University, in conjunction with the UK
Department of International Development, conducted
a study in the Kyuso District of Kitui County, Kenya,
from August 2012 until December 2013, in which
water pumps were equipped with GSM transmitters
that sent data via SMS over existing mobile phone
networks. Following a series of proof-of-concept
tests in Lusaka (July 2011), transmitters were installed
in 21 handpumps, that offered: (1) measurement of
handpump usage and associated volumetric water use;
(2) remote surveillance of maintenance service delivery
and downtime; and (3) objective data to improve
infrastructure planning and investment, and promote
sector accountability. Data transmitted from pumps are
captured in a relational database and presented using a
bespoke graphic user interface. The Ministry of Water
and Irrigation, the Water Services Regulatory Board
and the District Water Office were consulted for their
inputs. The results of the study are compelling: 98% of
handpumps now work in Kyuso (compared to a rough
estimate of 70% in Africa), five times more revenue is
being collected, handpump downtime reduced 10-fold
from 27 days to less than 3 days, and now 80% of
users are willing to prepay after the trial (compared to
less than 20% before the trial).
http://www.smithschool.ox.ac.uk/library/reports/SSEE_Rights to
Results_FINAL_March2014.pdf
SWEETSense - CellPump
SweetSense Inc. provides low-cost remote monitoring
solutions for water, energy, and environmental projects
to reduce client operating costs, improve technical
performance, and increase overall effectiveness. The
50

social enterprise corporation, in collaboration with
Portland State University’s SweetLab, has developed
CellPump, a smart sensor that can be installed within
the pump head of Afridev and India Mark 2 water
pumps. Water flow data are transmitted by sensors
over GSM cellular networks (98% of the country
has cell coverage) and monitored by Living Water
International via an online dashboard. Currently over
200 sensors have been provided for Living Water
International, deployed in various remote villages in
Rwanda. The project is supported in part by the GSM
Association and the UK Department for International
Development, and has been implemented in
partnership with MTN, the Rwanda Ministry of Natural
Resources, and other local government authorities.
http://www.sweetsensors.com/applications/cellpump/
WellDone - MoMo
WellDone builds technological tools that empower
resource-constrained communities with the data
they need to invest in and maintain lasting critical
infrastructure. The organization works at the local
level to ensure that these communities become
independent of development assistance, and focuses
on the distribution of technology that facilitates more
reliable and robust infrastructure development. Notably,
MoMo (mobile monitor) is a mobile device produced
by WellDone that collects data to track the progress
of infrastructure projects, report on their progress,
and notify appropriate personnel when maintenance
is required. MoMo has been used in handpumps,
measuring their functionality, their frequency of use,
and their hourly water flow. The data generated by
remote MoMos are then aggregated using WellDone’s
Smarter Villages software, which alerts local repair
teams by SMS whenever a well in their area breaks
down. The software also displays trends in water use,
which can be used at the local, and potentially national,
level to better allocate resources and plan projects
according to levels of need.
https://welldone.org
Agriculture Livelihoods
Kilimo Salama - Connected Weather Station
Beginning in 2009 with a pilot project offering index
insurance to 200 farmers in Kenya, the Syngenta
Foundation’s Kilimo Salama (“Safe Farming”) weather
index insurance programme has helped over 51,000
farmers in Kenya and 14,000 farmers in Rwanda
to date. The programme’s solar-powered weather
stations collect weather data every 15 minutes, which
are then aggregated and compared to historical
weather data at the end of each growing season.
Any payout owed is calculated and sent to farmers
via mobile phone. Syngenta, on behalf of Kilimo
Salama, partnered with Safaricom in 2010, providing
a less expensive and dense communications network
for product sales and customer communication.
Using Safaricom’s M-PESA mobile banking system,
the programme keeps index insurance premiums
affordable for smallholder farmers, who receive their
index insurance policy numbers and premium receipts
via SMS. Payouts are sent electronically through
M-PESA as well, which has reduced the associated
risks of adverse weather and provided farmers with
a financial safety net. Subsequently, the regions that
have participated in the Kilimo Salama programme
have enjoyed increased agricultural investment and
improved wellbeing, while partnering microfinance
institutions have seen their loan portfolios increase in
value, sometimes by as much as double within the first
six months.
http://www.ifc.org/wps/wcm/connect/
e0ed35804c33fc309479def12db12449/KS+story.
pdf?MOD=AJPERES
Nano Ganesh – Micro Irrigation Solution
In India, the Nano Ganesh solution is a low-cost
product set to provide small-shareholder farmers with
a tool that can remotely control their micro irrigation
pumps. In India, about 25 million water pumps are in
use for farm irrigation. Many of these pumps have to
be manually operated, based on rainwater conditions,
electricity availability and crop needs. For the average
small-scale farmer, the variability of these factors on a
51

day-to-day basis adds extra burdens in terms of time,
labor and fuel costs. In many cases, farmers need to
travel long distances and/or through difficult conditions
to access their pumps from their households. The
Nano Ganesh unit works by attaching to the irrigation
pump, and serving as an actuator, which can turn on
and off via basic commands from the farmer’s simple
feature phone (2G). The farmer is also able to check
the availability of electricity at the pump, as well as the
availability of water near the pump (with an additional
water sensor). As of August 2014, twenty thousand
farmers in India are benefiting from Nano Ganesh.
http://www.fao.org/3/a-i4622e.pdf
Sensors for Tea Plantations
The quality of tea, and its resulting sales price, are
determined in part by water composition of its soil
and surrounding environment. As a major export of Sri
Lanka and Rwanda, it is essential that moisture levels
be monitored and recorded; however, traditional manual
methods have proven inefficient and time-consuming.
Plantations in both countries utilize WSNs to monitor
moisture, pH levels, carbon, nitrogen, potassium, calcium
and magnesium levels of the soil in which their tea
grows. Sensors and connectivity modules are powered
by solar panels, and the data that they capture are
transmitted wirelessly. At a relatively low cost, sensors
deployed in the field provide actionable feedback every
fifteen seconds, saving time, money, and crops.
http://wireless.ictp.it/rwanda_2015/
http://www.ictp.it/about-ictp/media-centre/news/2015/6/
teatime-with-iot.aspx
http://wireless.ictp.it/school_2015/presentations/CaseStudies/
IoTforTeaPlantation-SriLanka-Minuri.pdf
Timor-Leste’s Community-Based IUU Reporting System
In Timor-Leste, the National Directorate of Fisheries
and Aquaculture (NDFA), working in partnership
with the FAO-Spain Regional Fisheries Livelihoods
Programme for South and Southeast Asia (RFLP),
introduced a plan using radio location beacons to
protect local waters from illegal fishing and to provide
emergency response to fishermen in distress. The
partnership introduced low-cost personal location
beacons (PLBs) as part of a community-based system
for identifying illegal, unreported and unregulated
fishing. Now, when local fishermen identify illegal
fishing boats in the Timor-Leste waters, the location
beacon is activated and data are sent to an Internet-
enabled mapping platform, which authorities utilize
to track and apprehend illegal fishing boats or initiate
rescue services.
http://www.fao.org/3/a-i4622e.pdf
Resiliency, Climate Change, Pollution Mitigation
Fresh Air in Benin
Urban air pollution is a major problem in many
developing country cities. The World Health
Organization (WHO) attributes one in eight deaths
worldwide to polluted air, as dirty air causes lung
damage, heart disease, strokes and cancer. The WHO
also estimates that indoor air pollution in homes in
Africa contributed to nearly 600,000 deaths in 2012.
To measure the extent of the problem, air quality
sensors are being deployed in a range of cities to track
levels and changes in pollutants. One such project,
Fresh Air in Benin, is focused on developing a network
of air quality sensors to capture and send data every
20 minutes via GSM connectivity.
https://docs.google.com/folderview?id=0B_
lA8alw74UiVGJuU0hGV01scVEusp=docslist_
apitid=0B81LFtxt5uZKeEphNG9PRTFPR2c
Smart Fire Sensors for Slums Informal Settlements
A regularly occurring tragedy in high-density urban
slums, fires are particularly deadly given the rapid
and haphazard nature of these neighborhoods’
development, including narrow doorways that are
often blocked, high population density, and the lack of
adequate first response. The Red Cross has proposed
that local entrepreneurs help solve the problem by
installing connected fire alarm systems across slums
and other informal settlements to quickly notify
residents when a fire first starts. The organization
has also explored the option of developing low-cost,
solar-powered sensors networked together to quickly
detect when a fire has broken out and notify the
52

dwelling’s resident(s), as well as surrounding neighbors.
Upon detection of smoke, the network sounds an
alarm and communicates the threat to other homes
nearby through SMS or other modalities. Additionally,
the networked sensors are GPS-enabled and have the
ability to identify a fire’s point of origin and transmit
that information to authorities so that they may more
accurately target fire extinguishment efforts. Currently,
the intervention is being tested in Nairobi and Cape
Town, with participation by two thousand households.
https://drive.google.com/file/
d/0B1vf6TLGIC0yZk9UU2t2UGFmNlE/view?usp=sharing
South Indian Ocean Tsunami Warning System
Following the 2004 Indian Ocean tsunami that
devastated coastal areas in India, Sri Lanka, Thailand
and Indonesia, the international community united to
establish the Indian Ocean Tsunami Warning System
whereby kinetic sensors (measuring waves and water
flow) placed on the ocean floor communicate data on
potential tsunamis to disk buoys floating on the ocean
surface via acoustic telemetry. Once collected, buoys
then upload the information to government authorities
via satellite connectivity.
http://www.kophuket.com/phuket/homemenu/tsunami.html
http://iotic.ioc-unesco.org/indian-ocean-tsunami-warning-
system/tsunami-early-warning-centres/56/national-tsunami-
warning-centres
Echo Mobile - Fleet Management for Public Safety
In Kenya, a large share of road traffic accidents occur
in the semi-formal public transport sector using
minibuses (known as matatus). With a third of the
population relying on informal transit, unsafe driving
practices are a substantial cause of preventable death.
The challenge is that mini-bus owners lease buses
daily to their drivers but cannot observe the drivers’
behavior. As most drivers are paid for each route they
complete, they may adopt reckless habits: speeding,
weaving between lanes, and driving on sidewalks.
Misaligned incentives lead to an unsafe transport
system particularly for passengers. Echo Mobile has
developed a sensor-based system for vehicle owners
to track where and how their vehicles are being driven.
The technology includes an in-vehicle sensor monitor
that reports on vehicle velocity, acceleration, heavy-
braking, and transmits data via GSM. A web application
collects and analyzes each device’s data streams,
launching notifications to stakeholders (driver, owner,
fleet manager, municipality). An Android application,
coupled with SMS alerts, gives owners access to
detailed real-time information about each vehicle
in their fleet. Through the application, owners (and
other stakeholders) can drill down into information on
locations, routes, and recent productivity and safety
events and take action against unsafe drivers.
https://www.echomobile.org
http://pedl.cepr.org/sites/default/files/Research%20Note_
Mitigating%20Market%20Frictions%20by%20Monitoring%20
SME%20Employees_3022.pdf
Natural Resource Management
Acoustic Sensors for Seabird Monitoring Identification
Funded with a grant from the National Science
Foundation, Nexleaf Analytics developed and tested
a low-cost tool to remotely monitor seabirds; test
deployments focused on colonies located in the
Pacific. From December 2011 through May 2012,
the organization worked with the U.S. Fish Wildlife
Service and the Coastal Conservation Action Lab
at the University of California at Santa Cruz to deploy
four sensors on Tern Island, located in the French
Frigate Shoals in the North-West Hawaiian islands. The
deployment consisted of four wildlife acoustic monitors
and two WiFi radio repeaters powered by 20W solar
panels, and one gateway computer. An estimated
400 MB of data were generated daily. The project
demonstrated the performance capabilities of satellite-
based WSNs for long-term monitoring of seabird
colonies, a method which may prove useful for other
projects dedicated to conservation efforts or natural
resource management.
http://nexleaf.org/project/tern-island
53

Anti-Poaching Interventions
Illegal poaching of large wildlife is a major concern
across Sub-Saharan Africa. In 2014 alone, 40,000
elephants were illegally killed, primarily for their tusks,
which are highly sought after in the ivory trade. From
26 million elephants in 1800 to fewer than one million
today, the African elephant is at risk of extinction
by illegal poaching, but it is not alone. Demand for
the horns of the black rhinoceros has lead to 96%
decline in the species’ population from 1970 to
1992, with fewer than 20,000 animals remaining.
To combat these and other poaching operations,
a wide number of projects have commissioned
emerging IoT technologies, such as connected drones
for surveillance, as well as long-range wide area
technologies, to track wildlife and to monitor activity at
the boundaries of game parks.
http://airshepherd.org/
http://gblogs.cisco.com/uki/week-4-how-can-technology-
help-the-anti-poaching-activities/
Wadi Drone
The Wadi Drone is a fixed wing airplane with a 2.5
metre wingspan carrying a small communications
payload that retrieves information from ground-based
scientific measurement devices. Comprising four NYU
Abu Dhabi students, the Wadi Drone development team
collaborated with the Emirates Wildlife Society (WWF)
and the country’s first national park, Wadi Wurayah
National Park located in Fujairah, to craft and deploy
the drone, which was a 2015 finalist in the UAE Drones
for Good Award competition. Within the parameters of
the national park, the drone flies over mountains and
through valleys to wirelessly download photographs
taken by ground-based camera traps that automatically
capture images of wildlife as they pass in front of the
camera’s motion sensor. Collecting data via permanent
communications infrastructure could interfere with the
natural environment or endanger workers, and the
Wadi Drone project assists the conservation efforts
of the Emirates Wildlife Society by both increasing
the rate at which photographic data of wildlife can be
analyzed by experts, and by reducing the human risks
associated with the current method of hiking to retrieve
photos from remote camera traps. Wadi Drones further
eliminate the need to employ a costly helicopter to reach
camera traps during the summer months, when high
temperatures make hiking conditions dangerous.
http://wadi.io
Rainforest Connection
Rainforest Connection transforms recycled smartphones
into autonomous, solar-powered listening devices
that can pinpoint signs of destructive activity at great
distance. It is a scalable, real-time logging detection
system, pinpointing deforestation activity as it occurs.
With an initial field project only located in Sumatra,
Rainforest Connection (RFCx) is now expanding to three
more endangered areas in the rainforests of Indonesia
the Amazon, and Africa. By partnering with local
communities, indigenous groups, and organizations that
are committed to responding to real-time interventions,
the organization hopes to demonstrate that their system
of next-generation devices can operate on a global
scale, in any forest, anywhere.
https://rfcx.org
54

Energy
M-KOPA Solar
M-Kopa, a pay-as-you-go Energy Service Company
(ESCO) for off-grid customers in Kenya, leverages
machine-to-machine (M2M) technology to fulfill
its mission of providing high-quality energy at an
affordable rate to everyone. Their solar home system
from D.Light Design is comprised of photovoltaic cells,
a battery system and a communications model, which
individuals purchase altogether at a discount with
capital costs amortized over a designated purchase
period. After the system is installed, customers are able
to utilize the electricity generated by the solar cells to
power home appliances. Following an initial deposit,
they must make regular payments (usually via mobile
money systems, e.g. M-Pesa or Airtel Money) to
continue using the device. M-Kopa remotely monitors
the amount of electricity captured, the allocation that is
stored, and, through the device’s connectivity module,
if the device is working appropriately.
http://www.m-kopa.com
Monitoring Evaluation of Cookstoves
Across the developing world, wood and charcoal-
burning cookstoves are used extensively to prepare
meals and to provide a source of heat inside homes.
The resulting indoor air pollution contributes to
approximately 4 million deaths a year, out of the 3
billion people worldwide who utilize biomass to prepare
their meals. In response, a number of initiatives are
in place to help lower-income households transition
to less injurious methods of meal preparation and
heating by way of improved cookstove projects. The
U.S. Government has supported a five-year initiative
of the “Global Alliance for Clean Cookstoves” which
aims to achieve a goal of enabling 100 million homes
to adopt clean and efficient cooking solutions by 2020.
IoT sensors play a major role in this initiative by helping
to measure in real time the black carbon emitted by
cookstoves, and also in the monitoring and evaluation
of projects that disseminate upgraded cookstoves. In
one particular project in the Sudan, the use of improved
cookstoves via sensor-enabled recording instruments
was compared to traditional survey data (captured by
an enumerator) to determine whether the latter method
was accurately reflecting usage captured by sensors.
In this case, the study found that survey participants
were over-reporting daily cooking hours by 1.2 hours
on average, and daily cooking events by 1.3 events.
The results suggest that survey-based methods for
monitoring and evaluation may be misleading in their
presentation of actual impact and usage. According
to the results of this study, data collected by sensors
therefore represents the more reliable method.
http://www.state.gov/r/pa/prs/ps/2015/09/247240.htm
Wilson, Daniel L., et. al. “Comparing Cookstove Usage
Measured with Sensors Versus Cell Phone-Based
Surveys in Darfu, Sudan.” Technologies for Development:
What is Essential.
55

Annex 2: Different Characteristics of Wireless IoT Connectivity Technologies
Wireless
Technology
Type
TechnologyNameMaxRange
MaxBandwidth/Data
Throughput
OperatingLife
(Battery)
ModuleCost
Spectrum/Operating
Frequency
SpectrumLicense
WPAN
ANT+30m1MbpsDays$1-$152.4GHzunlicensed
Bluetooth4.0LE50m24MbpsHours$1-$152.4GHzunlicensed
RFID
Passive:10m
Active:100m
100Kbps
PassiveTags:
n/a
ActiveTags:
years
Passive:
$1-$5
Active:
$5-$25
120-150kHz;12.56MHz,
433MHz,ISMbands(868
MHz,900MHz),2.5-5.8GHz
unlicensed
NFC10cm424Kbpsn/a$113.56MHzunlicensed
802.15.4g200m200KbpsUpto4years$1-$152.4GHzunlicensed
ZigBee10-100meters250Kbps
uptotwo
years
$1-$15
2.4GHz/900Mhz
(915MHz,868MHz)
unlicensed
WLAN
Wi-Fi300m
250Mbps(802.11n);
54Mbps(802.11a/g);
11Mbps(802.11b);
1Gbps(802.11ac)
4-8
hours(com)
50hours
(idle)
$10+2.4GHz/5GHzunlicensed
Wi-Fi(802.11ah)upto1000m100kbps(802.11ah)
Sub-1GHzISMbands–
Europe(863-868.6MHz);
Japan(950.8MHz–957.6zMHz);
Korea(917-923.5MHz);
USA(902-928MHz)
unlicensed
WWAN
LoRa2-10km200Kbps
10-20years
(idle),
120hours
communicating
$1-$15
ISMbands(868MHzin
Europe;
900MHzinUS)
unlicensed
Weightless2-10km200Kbps10years$1-$15
Weightless-N:ISMbands
(868MHzinEurope;
900MHzinUS);Weight-
less-W:TVWS
unlicensed
Dash72km200Kbps
Upto
10years
$1-$15433Mhzunlicensed
WiMax
40km
(30
miles)
34Mbps–1GbpsHours$1-$15
Nouniformgloballicensed
spectrumbutWiMAX
forumpublished3licensed
spectrumprofiles:2.3GHz;
2.5GHz;3.5GHz
licensed
2G(GSM,GPRS,EDGE)35km9.6Kbps–384Kbps
4-8hours
(com)
36days(idle)
$1-$15GlobalGSMbandslicensed
3G(UTMS,HSPA)upto100km384Kbps–10Mbps
2-4hours
(com)
20days(idle)
$35-$50
Various-
licensed
licensed
Cellular4G/LTEupto100km3Mbps–100Mbps
2-3Hours
(com)
12days(idle)
$80-$120
Various-
licensed
licensed
Note: Non-exhaustive. Source: Cisco Systems.
56

Annex 3: Sample Sensor Prices (Retail)
Sensor Name
Price
(rounded up)
Integrated WiFi High Temperature Sensor $204
Integrated WiFi Humidity Sensor $180
Dual-Range Force Sensor $110
Ultrasonic Range Finder; CO2 Sensor $100
Gas Pressure Sensor $83
Vernier Motion Detector $75
Oxygen Sensor $60
RFID Starter Kit; Voice Recognition Shield; Color Detector Sensor; JPEG Color Camera
TTL Interface; Soil Temperature and Moisture Sensor
$50
Wind Speed Sensor $45
Humidity Temperature Sensor $42
Multichannel Gas Sensor; Liquid Level Sensor; Soil Moisture and Temperature; Liquid Level Sensor;
GPS Breakout Sensor; Wearable GPS Module
$40
G5 Water Flow Sensor; RFID Reader; RFID Sensor Module; Ultrasonic Range Finder Lite;
Temperature and Humidity Sensor Board;
$30
Distance and Gesture Sensor; Liquid Flow Meter Nominal Thread; Ultrasonic Rangefinder;
Infrared Distance Sensor
$25
AttoPilot Voltage and Current; Pressure Sensor (up to 100 lbs); Pressure Sensor; High Accuracy
Barometer; Temperature and Humidity Sensor
$20
Humidity Sensor $17
G1 Water Flow Sensor; Camera Module; Dust Sensor; Weight Sensor 400kg $16
Ultrasonic Range Measurement Module; Barometric Sensor; Infrared Proximity Sensor; RGB and
Gesture Sensor; Temperature and Humidity Sensor; Triple-axis accelerometer; IR Distance Sensor
$15
Low Accuracy Barometer; Grove Gesture Sensor $14
Coulomb Counter; UV Sensor $13
AC Current Sensor high amperage $12
Sound Detector $11
Capacitive Touch Sensor; Load Sensor (up to 50kg); Air Quality Sensor; FM Receiver; Temperature
and Humidity Probe; Barometric and Temperature Sensor; Altitude Sensor; Digital UV Index/IR/Visible
Light Sensor; Proximity Light Sensor; Liquid Flow Meter; PIR Motion Sensor
$10
Current Sensor; PIR Motion Sensor; Collision Sensor; Temperature and Humidity HP Sensor $9
RGP Light Sensor; Alcohol Sensor; RGB Color Sensor and IR Filter $8
Infrared shooting sensor; AC Current Sensor; HDR Digital Light Sensor; Microphone Amplifier $7
Luminosity Sensor; Loudness Sensor $6
Infrared reflective sensor; RFID Capsule; Ambient Light Sensor; Moisture Sensor; Sound Sensor $5
Vibration Sensor; Water Sensor $3
Source: Website review of sensors prices August 6 – 11, 2015 from Sparkfun, Seeed Technology Inc., Adafruit, and Monnit.
57

List of Acronyms and Abbreviations
API Application Programming Interface
FDA Federal Drugs Agency (of the United States)
GSMA Global System for Mobile Association
IoE Internet of Everything
IoT Internet of Things
ITU International Telecommunication Union
M2M Machine-to-Machine
M2P Machine-to-Person
MDGs Millennium Development Goals
NFC Near Field Communications
P2P Person-to-Person
PLB Personal Localization Beacon
SDGs Sustainable Development Goals
UN United Nations
US United States
US$ United States Dollar
WHO World Health Organization
WLAN Wireless Local Area Network
WSN Wireless Sensor Network
WWAN Wireless Wide Area Network
4G Fourth-generation mobile
5G Fifth-generation mobile
58

Geneva, 2016
ISBN 978-92-61-16401-0
9 7 8 9 2 6 1 1 6 4 0 1 0
International
Telecommunication
Union
Place des Nations
CH-1211 Geneva 20
Switzerland

Harnessing io t-global-development

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