Systems using Wireless Sensor Networks for Big Data

Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
DOI:10.5121/cseij.2022.12605 35
SYSTEMS USING WIRELESS SENSOR NETWORKS FOR
BIG DATA
Richa Verma and Ravindara Bhatt
Department of Computer Engineering, Jaypee University of Information Technology,
Solan, India
ABSTRACT
Wireless sensor networks are continually developing in the big data world and are widely employed in
many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the
detected item. In recent years, WSN has also made important strides in the management of critical data
protection, traffic monitoring, and climate - change detection. The rich big data contributors known as
wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale
networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless
networks, suffer from significant constraints in communication and data dependability due to the cluster’s
constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data
systems Potential network and effective deployment and scientific problems are presented and discussed in
the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss
interesting future research possibilities.
KEYWORDS
Big data, Wireless Sensor Networks, Big Data Storage, Protocol Layering, Sensitive Data, Challenges of
Big Data in WSN.
1. INTRODUCTION
The fundamental objectives of sensor-driven networks are to discover and record events,
aggregate and report the acquired data, and oversee tasks. The main WSN participants are known
as wireless sensor nodes. These wireless nodes are made up of the mote and sensor devices. The
sensor detects some event or physical phenomenon and informs the mote about it. The following
components make up Mote: a wireless communication interface, a digitizer, a CPU, some sort of
storage device, and a power supply. Power storage and power harvesting are two feasible
categories into which the nodes' power supply might be segmented [1]. WSN are frequently
installed in isolated locations where post-installation maintenance by humans is not feasible. As a
result, efforts are being undertaken to increase their effectiveness and robustness. Deployment of
WSN is hampered by a number of factors, including power consumption and vast deployment
distances. These obstacles no longer stand in the way of widespread remote deployment due to
automation trends and newly emerging applications. Big data systems benefit from efficient data
aggregation and in-network processing. In order to enhance system performance and address
WSN's limitations, it is crucial to analyse research articles that merge WSN with big data
systems. As an example of a basic system design, consider about a massive data system that is
WSN-based. The data is sent to a temporary store by the sink node once it has been collected
from the sensor nodes for subsequent data aggregation. Following this, a big data framework
employing the primary store may operate with the combined data. Applications and big data
platforms manage the converted data [2].

36
Fig. 1 Large-Scale Network System [2].
WSN routing is much more complex than in typical ad hoc networks due to its unique qualities.
For WSN, a variety of routing methods that differ from the standard TCP/IP addressing scheme
have been suggested. These tactics concentrated on network characteristics as well as structural
and functional requirements. Being aware of the factors that are crucial to the sensor applications
is necessary for developing a better WSN strategy. It is possible to put sensor nodes in remote or
dangerous areas. This demands that nodes be able to communicate with one another even in the
absence of a base station or predetermined cluster of motes. Additionally, it might not be able to
replenish the sensor nodes' batteries [1]. This article provides a thorough analysis of recent
research on integrating WSN into large-scale data systems. In light of the study's objectives and
themes, potential network, deployment, and scientific issues are given and explored. In order to
explore exciting future research opportunities, outstanding topics are then discussed.
2. RELATED WORK
Hadoop, MapReduce, and Big Table are three of the most popular technologies and
methodologies for Big Data. Because they rapidly and affordably process massive volumes of
data, these advances have transformed data management along with quickness. Extremely huge
amounts of data with various structures may be processed with Hadoop (or no structure at all).
Among the Hadoop elements that make up their linkages are HBase, H Catalog, Pig, Hive, Oozie,
Zookeeper, and Kafka nevertheless, Hadoop Distributed File System (HDFS) and MapReduce
for Big Data are the most extensively utilized elements and well-known ideas [4]. The
connections between the various parts of the Hadoop ecosystem are shown in Figure 2.

37
Fig. 2 Hadoop ecosystem [4].
The components of a wireless sensor network (WSN) are geographically dispersed independent
devices that communicate digitally, acquire data, and identify particular incidents of relevance in
the physical and ecological circumstances. A network of randomly placed sensor nodes is created
and is uniquely recognized by serial numbers that are directly tied to node placements. When
querying events in the WSN, the network is directly alerted of the events of interest without
notifying the nodes beforehand. After gathering information about the requested event, the
network sends a report to the user. WSNs offer more opportunities and ease for the capture,
transmission, and analysis of massive data. For instance, wireless sensors are made smaller and
less expensive, sensor nodes are adaptable in their networking, WSNs place data at the center of
the network, the most valuable data is gathered from various vantage points and stages, and
transmitted collected data is used to process large amounts of data in order to improve the
accuracy of the information. The data gathered and processed do in fact expand exponentially as
the volume and deployment space of WSNs networks increase, needing planning node energy
usage. High-efficiency networking is crucial in the application of WSNs in order to increase the
energy utilization rate of a single node to a higher extent, lower the energy usage of the entire
WSN, and lengthen the life cycle of WSNs [3]. A wireless big data system that manages
enormous amounts of data across wireless networks. Several writers concentrated on network
connectivity, such as information propagation, information network management, and innovative
applications under protocol stack, as compared to detailing the full system. [2].

38
Fig. 3 Protocol layering of wireless big data systems [2].
2.1. Finding and Limitations
Table 1. Finding and Limitations
Sr.
No.
Paper
Finding Limitations
1. 1
The advantages, benefits, and
drawbacks of various solutions put
forth in the WSN and IoT arena are
then carefully considered.
Additionally, the research compares
different solutions based on
performance metrics like diversity,
integration, range of motion,
renewability, versatility, energy
efficiency, expandability, latency,
confidentiality, and big data.
Dealing with data structure collected
on a massive scale makes it difficult
to satisfy the stated need. As data
sources continue to increase, new
data processing strategies must
always be developed to accommodate
Bigdata.
2. 2
Outlined the technical research
problems for WSN-based big data
systems as WSN is considered one of
Resultant solution is not being
proposed for open
issues in the research area.

39
the key data sources.
3. 11.
In research, analyses of more than 130
papers from 2014 to 2021 have been
presented. The paper discusses a
number of areas of Industry 4.0,
including the designing stage, security
requirements, deployment stage,
network classification, problems, and
obstacles.
The region's current issues and
difficulties are not being adequately
addressed.
When it comes to the application and
deployment of Internet and Wireless
sensor in Sector 4.0, the safe and
reliable communication of the data to
a distant location is not being
prioritized.
4. 4.
Implemented data life cycle that makes
use of the Big Data technologies and
terminologies in order to increase the
effectiveness of data management.
Presents a data life cycle that makes
use of big data technology and
terminology, however future study
directions in this area are not chosen
based on methodology, findings, and
a number of unresolved problems.
5. 5.
The deployment of a large number of
nodes is supposed to highlight the
demanding specifications of WSN.
Ground level effects on WSN signals,
which affect data transmission between
nodes, have been taken into
consideration during experimental
evaluation to identify the various WSN
properties. For real-time Big Data
applications including border
surveillance, environmental
monitoring, and industrial processes,
the BDEG big data gathering method
has been developed.
Research is not focused on shortening
the total data transmission distance,
and additional factors like rate of bit
errors and ratio of signal to noise are
not taken into account in order to
further develop the BDEG algorithm.
6. 6.
By reducing energy consumption,
latency, PDR, throughput, false ratio,
PRR, and network overhead, the
performance metrics of WSN are
evaluated. The demand for routing
algorithms; the various types of routing
algorithms used to improve
performance; The most crucial element
in a wireless sensor network (WSN) is
QoS, while other factors should be
focused on enhancing security issues.
Networks are distant and exposed to a
variety of security risks that might
have a severe impact on performance.
When the network is used in
operation applications, such strategic
warfare, a solution to the issue is not
being addressed.
7. 7.
It is being evaluated the network
properties as well as the clustering
objectives of 37 cluster-based
methodologies while reviewing the
numerous network characteristics
supported by clustering (heterogeneity,
mobility, and so on). Unsurprisingly,
the data show that the main goal of
using such strategies is to decrease
energy use.
There are no further attempts being
made to address the issues of
mobility and heterogeneity through
clustering.
8. 8.
Explains the needs for managing
sensitive data’s security in the WSN
using big data technology. In response
to these issues, it applies the
application of WSN in the security
management of sensitive data and
Data security is the most often
utilized area, accounting for 38.41%
of all uses, followed by network
security (33.74%), and system
security (27.5%). The application
needs more enhancements overall

40
offers some assistance in problem
solving. It analyses the issues in the
security management of sensitive data
from many angles.
because it is not very thorough.
9. 9.
Explains how WSN and Big Data are
used in a collaborative firefighting
system in detail. The experiment was
carried out in a barren space. There
was equipment for WSN, Big Data,
and other subsystems. The experiment
employed just one FDU. Using a
microcontroller that was
preprogramed, it operated
independently. The HARMS (Human
Agent Robot Machine Sensor) protocol
was crucial to all of these
communications.
It would be preferable to utilize a
different microcontroller that can
support the JAVA programming
language in place of the one used in
WSN. WSN was unable to fully
utilize the HARMS model's benefits.
10. 10.
Networked models are used to show
how information outcome across
robotic wireless sensor networks may
maximize output while also
maximizing the amount of data
available.
Big data collection, sensing,
processing, storing, analysis, and
integration are still challenges for
intelligent industrial systems. More
visual monitoring systems need to be
studied and used.
3. UNRESOLVED BIG DATA CHALLENGES IN WSN
(1) Individuality and Cooperation: - Because IoT devices differ from one another,
compatibility is a problem. In the WSN or IoT environment, the different distributed
devices producing various types of data must connect [1]. As IoT applications and
connected devices become more diverse, ongoing work is needed in this area.
(2) Mobility: - Facilitating communication among fast moving objects, such as cars, and
exploiting mobility to improve communication effectiveness are difficult given the
impending advancement of IoT systems. It is obvious to address the issue of mobility for
things with some improved methods given the ubiquitous or widespread distribution of
things and the natural demand to facilitate their movement in IoT scenarios. Unlike a
traditional WSN, the mobility patterns in the networks may not always guarantee
connectivity between the source and the destination. As a result, feasible clustering and
mobility prediction algorithms should be created in accordance with the additional
restrictions of each network. In the network context, clustering algorithms also need to
be updated to take connectivity and mobility into account [1,2].
(3) Fog Computing and Real-Time Communications: - Big data systems strongly favor real-
time communication and processing because to the delay-sensitive nature of sensed data
in WSN. Supporting real-time communications is challenging with WSNs due to
significant node restrictions. Moreover, real-time tasks cannot be carried out by a big
data system built on a cloud computing architecture. Fog computing, which aims to
improve low latency, mobility, network bandwidth, security, and privacy, was recently
proposed to extend cloud services to the edge of the network by bringing computation,
communication, and storage closer to the edge devices and end users in order to support
real-time tasks. As a result, real-time processing can be supported by a big data system
based on fog computing. However, there isn't yet a standout WSN solution for real-time
communication [2].

41
(4) Delay: - Due to the enormous number of devices communicating with sensors and
producing various forms of data, numerous applications have experienced delays.
Particularly in WSN, IoT objects have typically been delay-sensitive objects. Numerous
factors, including topology, radio interference, mobility, etc., could be the cause of the
problem. With the IoT's ever-growing data sources, it is necessary to move in this path [
1].
(5) Model-based Simulation: - In research work, performance assessment and comparison
are crucial. Although numerous platforms have been proposed for big data and WSN,
there isn't a testing ground or simulator that integrates both of these technologies. It
seems feasible to integrate a WSN simulation into a big data simulator or platform for
broad tests as a temporary fix. On the other hand, creating an interface between two
systems to share data in a way that eliminates dependency is necessary for a long-term
solution. A modular and extensible assessment platform that can accommodate various
WSN types is also needed. Additionally, as evaluation is mostly dependent on
modelling, more accurate traffic and risk analysis is important for improving the
reliability of the experiment [2].
(6) Security: - Security for conventional WSNs is constrained to the relevant area. However,
because IoT systems enable communication between highly scattered objects over the
internet, people's perceptions of security are altered and improved. Despite the fact that
many researchers have included security and privacy in their suggested solutions, such as
blockchain technology, the fight against various forms of intimidation put forth by
attackers is constantly fascinating and thrilling. To link a blockchain, cryptography
processing and public key decryption are needed. Additionally, in order to transmit data
when constructing a new blockchain, the link's earlier and later sequences must be
confirmed. Therefore, various problems must be resolved in order to accomplish
immediate data updating and storage. Therefore, it is clear that improving security and
privacy is a problem that needs to be addressed [1,12].
4. CONCLUSIONS
In the circumstances where WSN was considered one of the key data sources, we examined
technological research concerns for WSN-based large data systems. Before describing the
potential applications for large data systems in WSN, we first discussed the key contributions of
each work. We concentrate particularly on wireless big data systems' protocol layering and large
data in WSN. The remaining unanswered questions in this field of study are then discussed.
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