Internet Of EveryThing

IOE Internet of every Things
MSc Student – IT College
16 Apr 2017
Ali Faiq Ali

© 2014 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 2
• What is IOT
• Element of IOE
• Big Data
• Storage Management
• Network Models
• Programming Facilitates the IoT
• Security Strategy
• Lightweight Cryptograph
• IOT lab

• The Internet has evolved in ways that we could never have imagined. In the beginning,
advancements occurred slowly. Today, innovation and communication are happening at
a remarkable rate.
• From its humble beginning as the Advanced Research Projects Agency Network
(ARPANET) in 1969, when it interconnected a few sites, it is now predicted that the
Internet will interconnect 50 billion things by 2020. The Internet now provides global
connections that make web surfing, social media, and smart mobile devices possible.
• The Internet: The Place to Go
normally, when people use the term Internet, they are not referring to the physical
connections in the real world. Rather, they tend to think of it as a formless collection of
connections. It is the “place” people go to find or share information. It is the 21st century
library, video store, and personal photo album.

• Governments are not immune to the change caused by rapid information exchange
through technology. Officials can respond quickly to emergencies through real-time data.
Citizens can connect through social media and gather support for change.
• Governments can embrace this technological change, and benefit from it, by
incorporating technology into the operation of a city. In 2011, the city council in
Barcelona, Spain launched the “Barcelona as a People City” project. This project uses
technical innovations to foster economic growth and the welfare of its citizens.

• Things
• For the IoE to function, all of the devices that are part of the intended IoE solution must
be connected together so that they can communicate. There are two ways in which
devices can be connected; either wired or wirelessly. Devices that are not traditionally
connected to the network require sensors, RFIDs, and controllers.
• Data
• Big Data refers to the vast amount of data generated every hour by billions of connected
devices. Big Data requires new products and techniques to manage, store, and analyze
it. Part of the solution to the problem of Big Data is virtualization and Cloud computing.
• Big Data refers to the way in which organizations collect and analyze vast stores of data
for insights that can help identify trends, predict behavior, and empower decision
makers.

• People
• Connected people make behavioral transformations based on their access to
information. Simultaneously, their changed behavior affects the information that is
generated. This is known as a feedback loop. Organizations use data generated by
connected people to refine and target their marketing strategies.
• Process
• Processes occur between people, things, and data. Today, the IoE brings them all
together by combining M2M, M2P, and P2P connections.

• In 2012, the number of connected devices on the Internet exceeded the world
population. This includes traditional computing devices and mobile devices, as well as
new industrial and consumer devices that we think of as “things”. Although, this may
seem like a lot of devices on the Internet, it represents less than 1% of the objects that
could be connected.
• Sensors are one way to collect data from non-computers. They convert physical aspects
of our environment into electrical signals that can be processed by computers. Some
examples are soil moisture sensors, air temperature sensors, radiation sensors, and
motion sensors. Sensors of all types will play an important role in connecting what has
traditionally been unconnected in the IoE

• A popular type of sensor uses radio frequency identification (RFID). RFID uses radio
frequency electromagnetic fields to communicate information between small coded tags
(RFID tags) and an RFID reader. Usually, RFID tags are used to identify and track what
they are embedded into, such as a pet. Because the tags are small, they can be
attached to virtually anything including clothing and cash. Some RFID tags carry no
batteries. The energy required by the tag to transmit information is obtained from the
electromagnetic signals that are sent by the RFID tag reader. The tag receives this signal
and uses part of its energy to power the response.
• he models shown in the figure have a transmission range of a few meters, while other
RFID tags are equipped with a battery and operate as a beacon that can broadcast
information at all times. This type of RFID tag usually has a range of a few hundred
meters

• Because of their flexibility and low power requirements, RFID tags are a great way to
connect a non-computer device to a network by providing information to an RFID reader
device. For example, it is now common to find car factories attaching RFID tags to the
car bodies. This allows for better tracking of that car throughout the assembly line.
• The first generation of RFID tags is “write once read many”. This means that they can be
programmed in the factory once, but cannot be modified out in the field. Newer RFID
tags are “write many read many”, with integrated circuits that can last 40 to 50 years and
be written to over 100,000 times. These tags can effectively store an entire history of the
asset to which they are attached, such as the date of manufacture, location tracking
history, multiple service cycle, and ownership.

• Sensors can be programmed to take measurements, translate that data into signals, and then send that data
to a main device called the controller. The controller is responsible for collecting data from sensors and
providing an Internet connection. Controllers may have the ability to make immediate decisions or they may
send data to a more powerful computer for analysis. This more powerful computer might be in the same LAN
as the controller or might only be accessible through an Internet connection.

• However, the IoT is only one of several market transitions that are enabling the full
potential of the IoE. For example, the following are transitions that are also enabling the
IoE’s full potential:
• Mobility - providing access to resources from any device, at any time, and from any
place
• Cloud Computing - providing distributed computing resources and services over a
network
• Big Data - as the volume of data being produced is accelerating, so too is our capacity
to analyze and process it
• IPv6 - expanding the current Internet address space by 3.4×10^38 addresses, easily
accommodating 50 billion devices by 2020, and billions upon billions more
• The amount of value an organization derives from the IoE depends on its ability to
capture transitions, such as cloud, mobility, and the IoT. For example, John mentions
Smart Grid. Smart Grid is a solution that realizes the benefit of the IoE by improving
energy efficiency.

•Kilobytes (KB) - Approximately one thousand (10^3)
bytes
•Megabytes (MB) - Approximately one million (10^6)
bytes
•Gigabytes (GB) - Approximately one billion (10^9) bytes
•Terabytes (TB) - Approximately one trillion (10^12) bytes
•Petabytes (PB) - Approximately one quadrillion (10^15)
bytes
•Exabytes (EB) - Approximately one quintillion (10^18)
bytes

• A driving factor of this growth of information is the number of devices connected to the
Internet, and the number of connections between those devices. But this is just the
beginning. New devices are being connected to the Internet daily, creating an abundance
of new data.
• With this amount of information, organizations must learn how to manage data and also,
how to manage “Big Data”.
• There are three primary dimensions of Big Data that must be accounted for: volume,
variety, and velocity. Volume describes the amount of data being transported and stored.
Variety describes the type of data it is. Velocity describes the rate at which this data is
moving. Data cannot move without infrastructure. The swiftness of infrastructure
(input/output, bandwidth, and latency) and the ability to rapidly enable optimal resources
(network, CPU, memory and storage) directly affects the velocity of data.

• Big Data applications receive information from a wide array of data sources, including PCs,
smartphones, tablets, machines, sensors, social media, and multimedia
applications. Much of this growth in data is due to mobile devices. Mobility enables
anytime, anywhere, any device, and any content-user engagement.
• Big Data refers to the way in which organizations collect and analyze vast stores of data
for insights that can help identify trends, predict behavior, and empower decision makers. It
considers:
• How much data is generated
• How this data is identified and managed as an asset to the organization
• How this data is turned into usable information
• How organizations use this data to make decisions
• Ask yourself, what happens when we share information or an opinion about a business on
a social network? How is this information propagated? Who gets this information? And
more importantly, how are businesses reacting and using this information to create new
customer connections?

• The exponential growth of data continues as the number of things connected to the
Internet increases. However, more data is not necessarily better if that data cannot be
accessed, analyzed, and applied in a usable manner. For data to be a true asset, it must
be used effectively. In addition, using old, inaccurate data wastes time, resources, and
money.
• Managing this increased amount of data creates many challenges, including:
• Bandwidth capacity on existing links connected to data centers
• Privacy concerns for user data
• Managing data for real-time communications
• Selecting and analyzing appropriate data

• As more and more things are connected to the Internet, the demand for bandwidth will
increase because of M2M communications in industrial, government, and home
applications.
• An essential characteristic of Cloud computing is broad network access. In Cloud
computing service models, enabling on-demand network access to shared computing
resources and services over the network results in increased requirements for network
bandwidth. In turn, higher bandwidth requirements demand infrastructure improvements.

• Historically, each computer has its own operating system, applications, and dedicated
hardware components. Now, using software emulation, several virtual computers can run
on a single physical computer. This means each virtual computer has its own operating
system, applications, and dedicated hardware components. This is known as
virtualization in computing. Each virtual machine, shown in the figure, operates
independently.
• In the corporate world, a single physical infrastructure can run multiple virtual
infrastructures. By virtualizing the servers and networks, companies can reduce
operational and administrative costs. The operational savings can come from the
reduction in power and cooling requirements and the number of physical machines. A
virtual server can be added to support additional applications.
• You can also use virtualization for your personal computing needs. You can try a new
operating system on your computer without damaging your current system. You can
browse the Internet safely with your virtual machine. The virtual machine can be deleted
if anything goes wrong.

• Cloud computing is another way to manage, store, and access data.
• Cloud computing involves large numbers of computers connected through a network.
Cloud computing providers rely heavily on virtualization to deliver their services. It can
also reduce the operational costs by using resources more efficiently. These companies
provide four distinct categories of services..
• Cloud computing allows the users to access their data anywhere and at any time. You
are probably already using some form of Cloud computing if you use web-based email
services.
• Cloud computing also enables organizations to streamline their IT operations by
subscribing only to needed services. By using Cloud computing, the organizations may
also eliminate the need for onsite IT equipment, maintenance, and management. Cloud
computing reduces costs for organizations. It reduces equipment costs, energy costs,
physical plant requirements, and support personnel training needs.

• Machine-to-Machine (M2M) connections occur when data is transferred from one machine or “thing” to
another over a network. These M2M connections are the most critical part of the Internet of Things.
• An example of M2M is a connected automobile that is signaling that a driver is almost home, which
prompts the home network to adjust the home temperature and lighting.
• M2P connections occur when information is transferred between a machine (such as a computer,
mobile device, or digital sign) and a person. Whether a person gets information from a database,
or conducts a complex analysis, this is an M2P connection. These M2P connections facilitate the
movement, manipulation, and reporting of data from machines to help people make informed
judgments.
• The actions that people take based on their informed judgments complete an IoE feedback loop.
For example, portable sensors and monitors can provide round-the-clock information on a
patient’s vital signs, but health care providers are ultimately responsible for using that information
to assess patients and provide treatment.

• People-to-People (P2P) connections occur when information is transferred from one
person to another. Increasingly, P2P connections happen through video, mobile devices,
and social networks. These P2P connections are often called Collaboration.
• As shown in the figure, the highest value of the IoE is realized when process facilitates
the integration of M2M, M2P, and P2P connections.

• We are embarking on a new wave of digitization by connecting the unconnected. To take
advantage of this wave of digitization, companies and cities are now increasingly
deploying IoT solutions. However, this rapid increase in growth has also introduced new
challenges including:
• How to integrate millions of things consisting of devices from different vendors each
using custom applications
• How to integrate new things to the existing network infrastructure
• How to secure these new devices, each configured with varying levels of security

• Client Server model – This is the most common model used in networks. Client devices request
services of servers. Servers are often located locally and managed by the organization. For
example, Microsoft Outlook is a client-server model where end users connect to the email server
using a locally installed email client.
• Cloud computing model – This is a newer model where servers and services are dispersed
globally in distributed data centers. Data is synchronized across multiple servers. Organizations
simply subscribe to different services within the Cloud. End users access applications from Cloud
servers without requiring an application-specific client. For instance, Gmail email is a cloud
service where end-users can access their email from anywhere without requiring a locally installed
application.
• Fog computing – This IoT network model identifies a distributed computing infrastructure closer
to the network edge. It enables edge devices to run applications locally and make immediate
decisions. This reduces the data burden on networks as raw data does not need to be sent over
network connections. It enhances resiliency by allowing IoT devices to operate when network
connections are lost. It also enhances security by keeping sensitive data from being transported
beyond the edge where it is needed.
• The Fog computing pillar basically extends cloud connectivity closer to the edge. It enables end
devices, such as smart meters, industrial sensors, robotic machines, and others, to connect to a
local integrated computing, networking, and storage system.

• Wireless and wired connectivity
• Quality of service queuing (for example, voice data before video data)
• High availability
• Secure transfer
• Infrastructure devices connect the individual end devices to the network, and can
connect multiple individual networks to form an internetwork. The management of data
as it flows through the network is a primary role of the infrastructure, or intermediate,
devices. These devices use the destination end device address, in conjunction with
information about the network interconnections, to determine the path that messages
should take through the network.

• As previously discussed, sensors and actuators are used abundantly in the IoT.
Specifically, sensors measure a physical property and forward that information across
the network while actuators perform actions based on a received signal.
• So how do the sensors know what information to capture or which controller to
communicate with? And how does the actuator know which action to perform or which
signals are required to activate that action?
• Sensors must be told what to capture and where to send that data. A controller must be
programmed with a set of instructions to receive that data and decide if it should process
and relay that data to another device. For example, IoT end devices, such as the
computer installed in a car, must be programmed to react to different road conditions.
• All of the devices in the IoT must be programmed. Therefore, people with programming
skills are critical to the success of the IoT and the IoE.

• This architectural approach reflects the service models of the Cloud Computing model,
taking advantage of Software as a Service (SaaS), Platform as a Service (PaaS), and
Infrastructure as a Service (IaaS).

• The larger and more integrated the IoE solution, the more decentralized the network
becomes. This allows for a greater number of access points into the network, which
introduces a greater number of vulnerabilities. A significant number of the devices
communicating across the IoE will be transmitting data from insecure locations, but those
transmissions must be secure. However, securing an IoE solution can be difficult due to
the large number of sensors, smart objects, and devices that are connected to the
network. The potential harm caused by allowing unsecured devices to access an
organization’s network is a significant challenge for security professionals.

• Firewalls - A firewall creates a barrier between two networks. The firewall analyzes
network traffic to determine if that traffic should be allowed to travel between the two
networks based upon a set of rules that have been programmed into it, as shown in the
Figure 1 animation.
• Intrusion Prevention Systems (IPS) - The IPS monitors the activities on a network and
determines if they are malicious. An IPS will attempt to prevent the attack by dropping
traffic from the offending device or resetting a connection. Click the numbers in Figure 2
to learn more about how an IPS works.

Lightweight cryptography is a branch of the modern cryptography, which covers
cryptographic algorithm sintended for use in devices with low or extremely low
resources. Lightweight cryptography does not determine strict criteria for
classifying a cryptographic algorithm as lightweight, but the common features of
lightweight algorithms are extremely low requirements to essential resources of
target.

Information technologies widely penetrate into people’s day-to-day activity. This is one of the main trends
of present-day society. An average man’s life cannot be imagined without various gadgets. A lot of
households use devices with an embedded operating system (besides usual personal computers), which
can be connected to the Internet and can even be united into a wireless network. Everywhere people are
surrounded by a variety of terminals, readers, sensors etc . Such expansion of smart technologies
crucially raises data security problems. However, now it is impossible to suggest a cryptographic
primitive that can be implemented in all types of target devices. We can tell that AES is a really strong
algorithm with good performance. It is absolutely advisable to use AES in high-end devices, in a large
variety of embedded systems or in some low-end devices (with several constraints). But it is impossible
to use common cryptographic algorithms in specific devices with extremely constrained resources The
examples of such devices include:
• RFIDs;
• low-end smart cards (including wireless);
• wireless sensors;
• indicators, measuring devices, custom controllers etc.

algorithms intended for use in devices with extremely low resources are slightly different from the design
criteria of commonly used cryptographic algorithms. This very specific field is covered by a branch of
modern cryptography – lightweight cryptography. Lightweight cryptography does not determine strict
criteria for classifying a cryptographic algorithm as lightweight, but the common features of lightweight
algorithms are extremely low requirements to essential resources of target devices, including the
following:
• size required for hardware implementation;
• computational power of microprocessors or
microcontrollers;
• random access memory (RAM);
• read-only memory (ROM) etc.

• - Block and stream ciphers (PRESENT, Spongent)
• - Hash functions and MACs (Quark, Marvin)
DESL & DESXL:-
DESL was proposed in . DESL is based on the classical DES algorithm . Unlike DES, DESL
uses a single S-box instead of 8 S-boxes of DES. The design criteria of the single DESL S-box
make DESL resistant to most common cryptanalytic attacks . This allows to save a part of ROM
for tables storage.DESXL is a lightweight version of the DESX algorithm , which is one of
widely used variants of DES. In contrast to DES, DESX performs input and output data
whitening with the specific sub keys. Like DESL, DESXL uses the same single S-box instead of
8 DESX S-boxes. Relatively low resource requirements of DESL/DESXL are just the result of
eightfold reduction of ROM requirements for tables storage (since this is the only difference
between DESL/DESXL and the classical algorithms). The authors of DESL/DESXL asserted in
that such reduction in requirements is enough to use the proposed algorithms in devices with
constrained resources with an example of passive RFIDs.

• Smaller block sizes: To save memory, lightweight block ciphers may use smaller block sizes than
AES (e.g., 64 or 80 bits, rather than 128). It should also be noted that using small block sizes
reduces limits on the length of the plaintexts to be encrypted. For example, outputs of a 64-bit
block cipher can be distinguished from a random sequence using around 232 blocks for some of the
approved modes of operations. Depending on the algorithm, this may lead to plaintext recovery,
key recovery or authentication tag forgeries with non-negligible probabilities.
• - Smaller key sizes: Some lightweight block ciphers use small key sizes (less than 96 bits) for
efficiency (e.g., 80-bit PRESENT). At the time of this writing, the minimum key size required
• - Simpler rounds: The components and operations used in lightweight block ciphers are typically
simpler than those of conventional block ciphers. In lightweight designs using S-boxes, 4-bit S-
boxes are preferred over 8-bit S-boxes. This reduction in size results in significant area savings.
For example, the 4-bit S-box used in PRESENT required whereas AES S-box . For hardware-
oriented designs, bit permutations (such as those used in PRESENT), or recursive MDS matrices
(as in PHOTON [] and LED []) may be preferred over complex linear layers. When rounds are
simpler, they may need to be iterated more times to achieve security
• - Simpler key schedules: Complex key schedules increase the memory, latency and the power
consumption of implementations; therefore, most of the lightweight block ciphers use simple key
schedules that can generate sub-keys on the fly. This may enable attacks using related keys,
weak keys, known keys or even chosen keys. When this is the case, it is necessary to ensure that
all keys are generated independently using a secure key derivation function (KDF)

• We propose to adopt new advancing technology, “Lightweight Cryptography”, in the IoT.
We describe two reasons that support our proposal.
• 1. Efficiency of end-to-end communication In order to achieve end-to-end security, end
nodes have an implementation of a symmetric key algorithm. For the low resource-
devices, e.g. battery-powered devices, the cryptographic operation with a limited amount
of energy consumption is important. Application of the lightweight symmetric key
algorithm allows lower energy consumption for end devices.
• 2. Applicability to lower resource devices The footprint of the lightweight cryptographic
primitives is smaller than the conventional cryptographic ones. The lightweight
cryptographic primitives would open possibilities of more network connections with lower
resource devices.

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