Routing Performances in Wireless Sensor Networks: Determining Shortest Path Algorithms Effectiveness

International Journal of Computer Networks & Communications (IJCNC) vol 16, No 6, November 2024
DOI: 10.5121/ijcnc.2024.16606 89
ROUTING PERFORMANCES IN WIRELESS SENSOR
NETWORKS: DETERMINING SHORTEST PATH
ALGORITHMS EFFECTIVENESS
Khine Thazin Min1
and N. Jeyanthi2
1
Faculty of Computer System & Technology,
Myanmar Institute of Information Technology (MIIT), Mandalay, Myanmar
2
School of Computer Science Engineering and Information Systems,
Vellore Institute of Technology, Vellore, India
ABSTRACT
Wireless Sensor Networks (WSNs) play a pivotal role in a myriad of diverse fields, ranging from crucial
healthcare applications to the creation of smart cities and effective environmental monitoring systems.
Within the realm of WSNs, the feature of efficient routing assumes paramount importance as it exerts a
profound influence on resource utilization and the overall performance of the network. In light of their
remarkable efficiency and straightforwardness, shortest-path algorithms have emerged as a prevailing
choice for routing mechanisms in WSNs. Hence, the primary objective of this specialized research is to
understand a comprehensive investigation into the calculating performance of sensor network routing, with
a particular circle on leveraging the potential of shortest-path algorithms. This ambitious effort will
involve the establishment of a rigorous evaluation framework encompassing various performance metrics,
enabling a meticulous assessment of the routing performance and the efficacy of the carefully selected
algorithms. The culmination of this research holds tremendous significance, as it is poised to bestow
invaluable insights into the practical implementation of WSNs. To attire with a comprehensive
understanding of their performance across various scenarios and metrics, the research community can
make informed decisions when choosing the most appropriate routing protocols suited to particular
Wireless Sensor Network (WSN) deployments. The potential results of this research promise to not only
improve the network's efficiency, reliability, and lifespan safe but also to pave the way for innovative
applications of Wireless Sensor Networks in critical areas of societal significance.
Keywords
Wireless Sensor Networks (WSNs), Routing Protocols, Shortest -path algorithms
1. INTRODUCTION
Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their
ability to collect data from the physical environment and facilitate various applications such as
environmental monitoring, surveillance, and industrial automation. In Wireless Sensor Networks
(WSNs), sensor nodes are deployed in a distributed trend to collect and transmit data wirelessly.
Effective routing is crucial for promising the reliable and timely delivery of data within wireless
sensor networks.
In WSNs, routing protocols are essential in deciding the route that data packets should follow
from source to destination. Shortest-path algorithms have become one of the most popular
routing techniques because they are straightforward and efficient in determining the shortest
distance between nodes. These algorithms attempt to maximize resource consumption while

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minimizing the cost metric, such as hop count or distance. The goal of what is being done is to
examine how well shortest-path algorithms in WSNs execute calculations for routing in sensor
networks. Insightful recommendations for the choice and improvement of routing protocols in
WSNs can be complete.
Objectives
a) To study and evaluate the existing studies on routing protocols, calculation performance,
and wireless sensor networks (WSNs).
b) To understand why shortest direct path algorithm is important.
c) To establish and track performance measures, such as packet loss, throughput and end
then delay, in order determine routing performance.
d) To conduct experiments and collect data to evaluate the computational capabilities of the
specified shortest-path algorithm (Bellman-Ford) in Wireless Sensor Networks (WSNs).
e) To compare and study the effectiveness and efficiency of different routing protocols
regarding their impact on the overall performance of the network.
f) To offer suggestions and insights based on the examination of computation performances
for choosing and improving routing protocols in WSNs.
Scope
The scope of this research includes the following key points:
 Comparisons of various performances such as throughput, end-to-end delay, packet loss
of DSR, DSDV, AODV routing protocols some of which usages shortest path algorithm
in relation to the data from the generated trace files.
 A simulation examining how different routing protocols affect the Quality of Service
(QoS) in Wireless Sensor Networks (WSNs).
2. RESEARCH BACKGROUND
2.1. Wireless Sensor Networks
Figure 1. Architecture of a Wireless Sensor Networks

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A Wireless Sensor Network (WSN) is a specialized of ad hoc network composed of numerous
small, autonomous, and resource-limited in wireless sensor nodes that work together to monitor
physical or environmental conditions in the base station. These sensor nodes are displayed with
various sensors to gather data on parameters such as temperature, humidity, pressure, light, and
other environmental factors. The nodes communicate wirelessly among themselves, forming a
self-organizing network that allows data to be relayed to a main base station or sink node. WSNs
are exploited in a wide range of applications, including environmental monitoring, agriculture,
healthcare, industrial automation, surveillance, and disaster management. Due to their low-power
design and ability to operate in exciting environments, WSNs offer benefits such as cost-
effectiveness, scalability, and real-time data collection. However, their resource constraints and
wireless nature pose challenges in terms of energy efficiency, network scalability, data reliability,
and security. To address these challenges, researchers and engineers are continuously exploring
novel algorithms, protocols, and energy-efficient techniques to enhance the performance and
extend the lifespan of Wireless Sensor Networks, making them a vital component in the realm of
the Internet of Things (IoT) and smart applications.
Table 1: Shortest Path Algorithms in WSN
Ref
#
Work Title Proposed
Method
Parameters Advantages Disadvantages
[20] Performance,
Dijkstra
Algorithm
in finding
shortest direct
path with
dynamically and
it avoids
malicious nodes
and intrusion.
Wireless sensor
network,
Dijkstra’s,
bellman ford,
distance vector,
random,
MATLAB
software
in estimation,
routing estimation,
less computational
density
reducing the
malicious node,
reduce network
traffic
[21] Floyd-Warshall's
algorithm for
shortest path
routing in wireless
sensor networks
to obtain all the
available
shortest paths
algorithms
Floyd-Warshall's
algorithm
a directed graph,
Presence of
acknowledgement
path of every
traversed path.
not exactly
sensor nodes in
WSN.
[22] Emergency
Services in Future
Intelligent
Transportation
Systems based on
Vehicular
Communication
Networks
emergency
services
Road safety will
evolve with the
blending of
vehicular
communication
networks with
road
transportation
Vehicular ad hoc
networks, road
safety,
emergency
services, inter-
vehicular
communication
direct
communication
automatic delivery
of accident-related
data to the Control
Unit, preliminary
assessment of
damages based on
communication and
information
processing
low level of
fatalities
significantly
improving the
response time
Efficient use of
resources
[23] Wireless Sensor
Networks Based
on Shortest direct
Path Algorithms
to perform
shortest path
algorithm in
wired and
wireless
communication
networks
Shortest path,
protocol, sensor
nodes, Dijkstra’s
Algorithm,
Bellman ford
robustness and
scalability of face
routing is
undeniable
Employed as an
alternative routing
protocol, easy to
implement
wireless graph
generally has
relatively few
adjacent nodes

92
[24] Cluster Head
Election in
Wireless Sensor
Network: A
Comprehensive
Study and Future
Directions and
limits
on deterministic,
probabilistic,
adaptive, fuzzy
logic, clustering
and cluster head
election.
Deterministic,
Adaptive,
Probabilistic,
Multi-Attribute
Decision Making
Schemes Fuzzy
Based Cluster
Head Election
Schemes.
 Random
Clustering.
 Dynamism in
Cluster Head
election.
 Role and Election
of Super Head
Cluster.
Node Mobility
Random
clustering and
re-election of
CH when the
depletion of the
energy of CH is
huge network.
[25] Using of Bellman
Ford's Algorithm
in WSN to
Identify the
Shortest Path
algorithms,
increase Battery
Power production
& Control the
DDOS Attackers
and Monitor the
base station.
the loop
formation,
malicious nodes
improving the
performance by
initializing
variable
randomly
Wireless Sensor
Networks,
Dijkstra’s,
Bellman-Ford,
Distance Vector
Algorithm,
Random,
MATLAB
software
-all available wired
network protocol
- ad hoc wireless
networks with less
modification
Node
deployment,
Energy,
consumption,
Data Reporting
Model, Link
Heterogeneity,
Fault Tolerance
2.2. Shortest Path Algorithms
Shortest Path Algorithms are vital in Wireless Sensor Networks (WSNs) because they are key to
efficient and reliable data routing. Within WSNs, these algorithms are used to identify the best
paths for transmitting data from source nodes to destination nodes, taking into account factors
such as connection quality, energy consumption, and network congestion. One widely used
algorithm for finding shortest paths in WSNs is Bellman-Ford algorithm.
The Bellman-Ford algorithm is a distance-vector routing protocol applied in Wireless Sensor
Networks (WSNs). Bellman-Ford does not require a complete network topology but relies on
iterative updates to the distance vectors. Each node maintains its distance vector and exchanges it
with its immediate neighbors. The algorithm then iteratively updates the distance vectors until
convergence. Bellman-Ford is more suitable for dynamic networks or scenarios where
maintaining a complete network view is impractical.
This algorithm plays a critical role in ensuring efficient data routing in WSNs. It enables the
nodes to determine the best paths for transmitting data packets, optimizing energy consumption,
reducing latency, and enhancing network reliability. As WSNs often operate in resource-
constrained environments, the choice of the appropriate shortest path algorithm becomes vital to
maximize network performance and ensure the longevity of sensor nodes. The current research
and optimization of these algorithms enhance the development of Wireless Sensor Networks
(WSNs), procedure them reliable and adaptable solutions for various applications, including
environmental monitoring, smart agriculture, healthcare, and industrial automation.
3. ROUTING PERFORMANCES IN WIRELESS SENSOR NETWORKS
Data transmission in sensor networks employs two techniques: Flooding and gossiping protocols.
Flooding involves broadcasting data packets to all neighbors until they reach their destination or
reach the maximum hop limit. Flooding is easy to implement but can lead to resource wastage
and overlapping.

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Gossiping protocol, a more advanced version of flooding, randomly selects a neighbor to transmit
data and continues this process. Unlike flooding, it avoids implosion issues but introduces delays.
Routing protocols fall into various categories, as shown in a Figure 2.
Figure 2. The Categories of Routing Protocols
3.1. Routing Discovery
Routing protocols are critical in Wireless Sensor Networks (WSNs) because they rule how data is
transmitted from source nodes to destination nodes within the network. With resource-
constrained and energy-limited nodes in WSNs, efficient routing is crucial for optimal
performance and network longevity. These protocols can be broadly categorized into Proactive
(table-driven) and Reactive (on-demand) types.
Both proactive and reactive protocols have unique advantages and are chosen based on specific
WSN requirements. The choice of routing protocol and shortest path algorithm significantly
impacts network performance, energy efficiency, and scalability. Efficient routing protocols in
WSNs are vital for reliable data transmission and enable applications like environmental
monitoring, smart healthcare, and industrial automation, enhancing the overall usability and
effectiveness of WSNs.
3.1.1. Proactive Routing
Proactive routing protocols maintain current routing information at all areas by continuously
updating routing tables as the network topology changes. This constant maintenance allows for
faster data forwarding, making them suitable for applications requiring low-latency
communication. Examples of proactive routing protocols include Optimized-Link State Routing
(OLSR) and Destination-Sequenced Distance Vector (DSDV).
3.1.2. Reactive Routing
Reactive routing protocols do not maintain routing information proactively but as an alternate
establish routes on-demand when a data packet needs to be transmitted. This reduces control
overhead and conserves energy in the network. Ad-hoc On-Demand Distance Vector (AODV) is
a popular reactive protocol used in WSNs. Reactive protocols, including AODV, employ shortest
path algorithms like Bellman-Ford to determine the best paths when needed.

94
Figure.3. Types and Classifications of Routing Protocols
3.1.3. DSDV (Destination-Sequenced Distance Vector)
DSDV is a proactive (table-driven) routing protocol in which each node maintains a routing table
containing information about the shortest paths to all other nodes in the network. These tables are
updated either periodically or whenever a network topology change occurs. DSDV is useful for
static or slowly changing network topologies.
Example: Suppose a WSN is deployed for monitoring environmental parameters in a remote
forest. The nodes in this scenario may not move frequently, making DSDV an appropriate choice
for maintaining stable routes.
Table 2. Wireless Sensor Networks in practice
Ref
#
Work Title Proposed Method Parameters Advantages Disadvantages
[11] GPSR: Greedy
Perimeter
Stateless
Routing for
Wireless
Networks
Dynamic Source
Routing. scalability
on densely deployed
WSN
GPSR High packet
delivery ratio
High delivery
success rate
Location
Database
overhead
Routing Protocol
overhead
[12] ASCENT:
Adaptive Self-
Configuring
Ensor Networks
Topologies
Motivates and
describes the
ASCENT
experiments
conducted on our
wireless testbed.
ASCENT Implementation
Evaluation
Design of
ASCENT
Self-
configuration of
Wireless Sensor
Networks
[13] Routing
Security
Problems in
Wireless Sensor
Networks:
Attacks and
a co-operated sensor
node could then be
used to launch such
malicious activities
as advertising false
routing information,
Attacks and
Defenses
cryptography,
key management,
secure routing,
secure data
aggregation, and
interruption
eavesdropping,
unauthorized
access, spoofing,
replay, and
denial-of-service
(DoS) attacks

95
Defenses and launching DoS
attacks from within
the sensor network.
detection in
WSNs
limited memory,
CPU,
communication
bandwidth, and
especially battery
life
[14] Instrumenting
the World With
Wireless Sensor
Networks
detailed physical
monitoring and
manipulation offer
enormous
opportunities for
almost every
scientific discipline,
*the feasible
granularity of
engineering
the design of
wireless
sensor net-
works and
discuss several
algorithmic
approaches
communication
energy costs
exceed their
benefits
massively
distributed,
physically-
coupled,
wirelessly
networked, and
energy limited
[15] Wireless-Sensor
and Actuator
Networks for
Smart Cities
WSN–IoT
interaction in an
opportunistic
manner so as to
significantly reduce
the energy
consumption of the
WSN nodes, the
authors presented a
prototype integration
platform.
WSN-IOT
Smart Cities
on WSANs for
smart cities will
continue to
uncover many
outstanding issues
realizing smart
cities and
communities
[16] End-to-End
Delay and
Energy
Consumption in
a two Cluster
Hierarchical
Wireless Sensor
Networks
rotating from time to
time the allocation
of temporary slots to
CHs in the TDMA
communication
Wireless
Sensor
Network;
Markov
process;
protocol;
Frame Slotted
ALOHA;
TDMA; E2E
delay
performance
indicators
flow
delay
lifetime of the
resulting wireless
sensor networks.
received no
external funding
[17] Walking GPS:
A Practical
Solution for
Localization in
Manually
Deployed
Wireless Sensor
Networks
present the design,
implementation and
evaluation of a
simple, practical and
cost effective
localization solution,
called Walking GPS,
that can be used in
real, manual
deployments of
WSN.
Walking GPS design,
implementation
the evaluation of a
localization
solution
deployment rate,
deployment
altitude,
sensor trajectory
the actual
location at the
beginning of
deployment
[18] An Assisted
living Oriented
Information
System Based
on a Residential
Wireless Sensor
Network
WSNs to open new
research
perspectives for ad
hoc deployment of
multi-modal sensors
and improved
quality of medical
care.
Alarm Net
designed for
smart
healthcare
Portability and
unobtrusiveness
Ease of
deployment and
scalability
Real-time and
always-on
Motion sensors
are positioned on
the walls in every
room to detect
movements and
presence in the
entire smart

96
Reconfiguration
and self-
organization
environment.
[19] The Effects of
Ranging Noise
on Multihop
Localization: An
Empirical Study
ranging
characteristics affect
Multihop
localization in
wireless sensor
networks
Multihop
localization
No Noise
Gaussian Noise
Sampled Noise
Many Multihop
localization
algorithms have
yielded
3.1.4. AODV (Ad-hoc On-Demand Distance Vector)
AODV is a reactive (on-demand) routing protocol that establishes routes only when needed. It
discovers paths using route request (RREQ) packets and route reply (RREP) packets. AODV is
more suitable for dynamic networks, as it minimizes control overhead and conserves energy.
Example: In a mobile WSN for monitoring wildlife, where the sensor nodes frequently change
positions, AODV can efficiently establish and maintain routes as the nodes move.
3.1.5. DSR (Dynamic Source Routing)
DSR is also a reactive protocol that utilizes source routing. When a node wants to transmit data, it
includes the entire route in the packet header. In between nodes are required to forward packets
only based on the route specified within them. DSR is flexible and works well in dynamic
environments.
Example: DSR can be applied in a Wireless Sensor Networks (WSNs) used for surveillance in a
crowded event. The nodes need to adapt quickly to changing routes as people move through the
part.
3.1.6. Flooding Technique
Flooding in wireless sensor networks is a communication technique where a message is broadcast
from one sensor node to all other nodes in the network. While it ensures message delivery to all
nodes, it comes with significant drawbacks like redundancy, increased energy consumption, and
scalability challenges.
For example, consider a forest monitoring system where sensor nodes need to alert all others
about a detected fire. Flooding entails each sensor node broadcasting the fire alert to all of its
neighbors who then relay the message to their neighbours, continuing this process until all nodes
have been informed. While effective in ensuring everyone knows about the fire, it results in
excessive communication, leading to quicker depletion of sensor node batteries and network
congestion.
3.2. Bellman Ford Algorithm
Even when a weighted graph has edges with negative weights, Bellman-Ford is a single source
shortest path algorithm that determines the shortest path between a given source vertex and
every other vertex in a network. In 1958, Lester Ford and Richard Bellman presented it. Both
directed and undirected graphs can be efficiently processed using the approach. As a single
source shortest path algorithm, Bellman-Ford's approach determines the shortest path from one
source node to every other node in a directed weighted network.

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The network's weights may even be negative figures. It employs a dynamic programming
methodology in which all potential solutions are evaluated before the best one is chosen. In the
following algorithm, all the edges of the networks are relaxed 𝑛 − 1 times, where n is the number
of nodes, and the distance of each edge is calculated in every repetition. Bellman-Ford is useful
for negative weight scenarios, like traffic delays. It can't handle negative cycles. It has O(V*E)
time difficulty, less efficient than Dijkstra's. Still, it's vital in network routing, traffic engineering,
and transportation systems due to its dynamic programming and versatility.
Figure.4. Flow Chart of Bellman- Ford Algorithm
3.3. Dijkastra Algorithm
Dijkstra's algorithm, named after its inventor Edger W. Dijkstra’s an algorithm for the shortest
paths between nodes in a weighted graph. The algorithm is commonly employed in applications
like network routing, maps, and transportation systems.
Here's a brief overview of how Dijkstra's algorithm works:
Initialization- Start by selecting a source node and set its distance from itself to 0, while all other
nodes are initially marked with infinity. You also maintain a list of unvisited nodes, initially
containing all nodes in the graph.
Exploration- involves repeatedly selecting the unvisited node with the smallest known distance
(from the source node) and marking it as visited.
Update Distances- For the selected node, compute the distance to its unvisited neighbors through
the currently explored path. If the newly calculated distance is shorter than the previously known
distance, update the distance value accordingly.

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Repeat- Continue the process of selecting the unvisited node with the smallest distance and
updating distances until you have visited all nodes or until the target node is reached.
Optimal Path Reconstruction- involves reconstructing the shortest path from the source to the
target by backtracking from the target node to the source node using the stored information about
the optimal path once all nodes have been visited or you have reached the target node. Dijkstra's
algorithm guarantees the shortest path when all edge weights are non-negative; however, it may
not work correctly when negative edge weights are present in the graph, in which case you might
need to use other algorithms like the Bellman-Ford algorithm.
4. EXPERIMENTAL SYSTEM AND RESULT DISCUSSION
NS2 (Network Simulator 2) is a widely used open-source network simulation tool that allows
researchers and network engineers to simulate and examine various network protocols and
scenarios. It is commonly used for academic research, performance evaluation, and protocol
development in the field of computer networking. It enables researchers and network engineers to
study various networking scenarios and protocols and gain valuable insights into network
performance and behavior.
4.1. Generating NAM Files and Trace Files
Once NS2 is set up and running on our PC, we can use it to simulate network scenarios and
generate trace files. Nam is a network animator that helps visualize the simulation results in a
graphical form.
To generate a Nam file and various trace files for energy consumption, throughput, and end-to-
end delay, we need to modify a TCL script to define your network, nodes, protocols, and
parameters.
Run the simulation with NS2, generating trace files for energy, throughput, and delay. Analyze
results using Nam to visualize the network and study trace files for metrics.
4.2. Comparison Of Routing Protocols In Wsns
A detailed comparison of WSN routing protocols is given below in tabular form: Table 3 and 4.
Table.3. Simulation Parameters for Mobile Nodes
Simulation Parameters Values
Number of Nodes 10,20,50,70,100
Simulation Time 80 s
Simulation Area 500 * 500
Data Rate of Nodes 2Mbps
Packet Size 512 bytes
Traffic UDP, CBR
Routing Protocols AODV, DSDV, DSR

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Table.4. Simulation Parameters for Fixed Nodes
Simulation Parameters Values
Number of Nodes 17
Simulation Time 25 s
Simulation Area 956 * 600
Packet Size 1500 bytes
Traffic TCP,FTP
Routing Protocols AODV, DSDV, DSR
4.3. Routing Metrics
 Minimum hop: Minimize total cost of data propagation and low end to end delay
 Energy: energy consumed per packet , total reserved energy
 Quality of service: latency(delay), throughput, packet loss, jitter, error rate
 Robustness: link quality, link stability
4.4. Performance Analysis of Routing Protocols
For our simulation, we will be concentrating on Quality of service (QOS) where end-to end
Delay measures the time it takes for a packet to travel from the source node to the destination
node. It is calculated by subtracting the time the packet was sent from the time it was received at
the destination as shown in fig 5.
Network Throughput metric represents the rate at which data packets are successfully delivered
from the source to the destination in the network. It's typically measured in bits per second (bps)
or packets per second (pps). To calculate in the network throughput, you can divide the total
amount of data successfully transmitted over a specific time period by that time period's duration.
Packet Loss Rate metric quantifies the percentage of data packets that do not successfully reach
their destination. To calculate the packet loss rate, divide the number of lost packets by the total
number of packets sent.
Figure 5. Simulation Model

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5. RESULTS AND ANALYSIS
After running the simulations for the network with parameters details in table.1, the following
results is obtained.
5.1. Mobile Nodes - End To End Delay
The end to end delay results as shown in Table 5.
Table.5 End to End Delay
No. of Nodes AODV (ms) DSDV (ms) DSR (ms)
0 0 0 0
10 0.6 0.5 1.9
20 0.728257 0.72 1.59
50 1.412 3.427 1.24
70 8.57 1.7019 9.45
100 1.048 7.822 2.300
Figure.6. Average End to End Delay
AODV performs well with low delay in smaller networks but experiences increased delay as the
network size grows, indicating potential routing overhead and congestion issues, as shown in figure
6.DSDV tends to perform well with low delay in smaller networks but experiences increased
delay in larger networks due to control packet overhead. The delay may vary as the protocol
adapts to network conditions.
DSR routing protocol appears to perform moderately well with low to moderate delay in smaller
networks. However, as the network size grows beyond a certain point, “There is a significant rise
in average delay.” suggesting potential issues with scalability and congestion management in the
network.

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5.2. Mobile Nodes - Throughput
AODV routing protocol demonstrates efficient data transfer with high throughput in smaller
networks as shown in table 6.
Table.6 Throughput
No. of Nodes AODV (bps) DSDV (bps) DSR (bps)
0 0 0 0
10 0.011232 0.0298882 0.18001
20 0.0257695 0.03073 0.1606
50 0.0899 0.15872 0.1218
70 0.0493 0.09333 0.0618
100 0.0562 0.034 0.0468
Figure.7. Average Throughput
"Although there is a reduction in throughput when the network reaches 50 nodes, AODV seems
to adapt and sustain stable throughput as the network continues to grow” as shown in figure 7.
This suggests reasonable scalability and performance in larger networks.
DSDV routing protocol demonstrates moderate data transfer efficiency in smaller networks.
While “there's been a rise in throughput "at 50 nodes, it appears that DSDV may encounter issues
with scalability and stability as the network size further increases.
DSR routing protocol initially demonstrates efficient data transfer with high throughput in
smaller and moderately sized networks. However, as the network size increases, "there has been a
marked reduction in the average" throughput, likely due to congestion and resource contention.
While DSR maintains some degree of stability in larger networks, achieving high throughput
becomes more challenging.

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5.3. Mobile Nodes - Packet Loss Rate
The packet loss rate results as shown in Table.7 and Figure.8.
Table.7 Packet Loss Rate
No. of Nodes AODV DSDV DSR
0 0 0 0
10 13.556 0.3 180.445
20 19.375 1.34 193.36
50 68.34 21.19 167.569
70 135.002 188.1 145.000003
100 99.36 90.3 119.4
Figure.8. Packet Loss
AODV routing protocol initially exhibits good packet delivery with a low packet loss rate in
smaller and moderately sized networks. However, as the network size grows beyond a certain
point "there is a clear increase" in the average packet loss rate. This behavior suggests that
AODV may face scalability challenges and increased packet loss in larger networks.
DSDV routing protocol demonstrates good packet delivery with a low packet loss rate in smaller
and moderately sized networks. This behavior suggests that DSDV may face scalability
challenges and increased packet loss in larger networks.
DSR routing protocol initially exhibits challenges with high packet loss rates in smaller and
moderately sized networks. However, it shows an unexpected improvement in packet delivery at
50 nodes, which is maintained in larger networks. This behavior suggests that DSR may adapt to
network conditions and perform reasonably well in larger networks.

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5.3. Fixed Nodes
After running the simulations for the network with parameters discussed in table.4, the following
results is obtained in table 5 and as shown in figure.9.
Table.5. Fixed Node Simulation Results
Average Throughput
(kbps)
Packet Delivery Ratio
AODV 0.248707 0.993304
DSR 0.287946 0.983425
0
0.2
0.4
0.6
0.8
1
Average Throughput (kbps) Packet Delivery Ratio
AODV
DSR
Figure:9 Fixed Node Simulation Results
AODV appears to establish a balance between Average, Throughput and Packet Delivery Ratio,
making it a robust routing protocol for wireless sensor networks. Its effectiveness can be
attributed to the use of the Bellman-Ford Shortest Path Algorithm, which helps in calculating
efficient routes on-demand, thereby optimizing data delivery.
DSR demonstrates a higher data transfer rate, but its slightly lower PDR suggests that it might
experience occasional packet losses. However, DSR's adaptability to changing network
conditions and source routing approach can contribute to its performance.
In summary, AODV, with the assistance of the Bellman-Ford Shortest Path Algorithm, appears to
offer a well-balanced performance in terms of data throughput and packet delivery in wireless
sensor networks. DSR, while showing better data transfer rates, may require further optimization
for packet delivery reliability.
In terms of Residual Energy
AODV exhibits the highest residual energy levels, indicating good energy efficiency and longer
node lifetimes. DSDV follows closely behind AODV in terms of residual energy, suggesting
stable energy consumption. DSR lags in terms of residual energy, with some nodes having
considerably lower energy levels, potentially impacting its overall network performance.
AODV and DSDV appear to be more energy-efficient and suitable for maintaining network
connectivity over an extended period, while DSR may require additional energy management
strategies to ensure network longevity and reliability. Selecting a routing protocol should
consider the specific requirements and limitations of the wireless sensor network.

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6. CONCLUSIONS
The results have provided an in-depth analysis of routing protocols, specifically AODV, DSDV,
and DSR, in the dynamic realm of wireless sensor networks. These routing protocols are the
backbone of data routing, ensuring efficient communication within these networks.
One pivotal aspect that emerged from our study is the utilization of the Bellman-Ford algorithm
within the DSDV protocol. This algorithm is instrumental in determining the shortest path
between nodes in the network The implication of this approach lies in its ability to effectively
handle various graph scenarios, particularly those with negative edge weights.
The Bellman-Ford algorithm, employed by DSDV, calculates the shortest path based on hop
counts or other metrics. This results in highly efficient routing decisions, conserving energy and
resources in resource-constrained wireless sensor networks. The algorithm incorporates sequence
numbers, a crucial feature for preventing routing loops. This enhances the reliability of the
routing protocol, particularly in dynamic and ever-changing network topologies. Shortest path
algorithms like Bellman-Ford offer deterministic route calculation, ensuring consistent When
discussing routing results across nodes. This predictability is advantageous for application
developers and network administrators.
The research also observed that the shortest path algorithms have certain limitations. While
effective, maintaining routing tables and periodic updates can introduce overhead, especially in
networks with rapidly changing topologies. This overhead can consume valuable network
resources. Shortest path algorithms may face scalability challenges in large and highly dynamic
networks. As the network size increases, the computational complexity of calculating and
maintaining routes also grows. Shortest path algorithms, while striving for optimality, may not
always account for real-time factors or network conditions. This can lead to increased end-to- end
delay, particularly in scenarios with high traffic loads.
7. SUMMARY
This research has provided valuable insights into the behaviour of these routing protocols. Under
different conditions and network sizes This emphasizes the critical importance of selecting the
most appropriate routing strategy to suit the specific needs of wireless sensor networks.
In summary, my study contributes significant insights into routing protocol performance within
wireless sensor networks. The combination of the Bellman-Ford algorithm in DSDV emphasizes
the importance of optimized routing decisions for efficient data delivery. While shortest path
algorithms offer advantages such as optimized routing and loop prevention, they also present
challenges related to overhead, scalability, and latency. These findings are pivotal for network
designers, researchers, and practitioners aiming to design and deploy efficient routing protocols
in the dynamic landscape of wireless sensor networks.
8. FURTHER RESEARCH
Future research endeavors should focus happening attractive routing protocols to address
scalability concerns, reduce overhead, and adapt to dynamic network conditions. Additionally,
investigating hybrid routing approaches that combine the strengths of different protocols,
including those employing shortest path algorithms, can further optimize routing performance.

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ACKNOWLEDGEMENTS
We would like to express our heartfelt gratitude to School of Computer Science Engineering and
Information Systems, Vellore Institute of Technology, ASEAN-India Research Training
Fellowship scheme of the Department of Science and Technology, Government of India, in
collaboration with the Federation of Indian Chambers of Commerce and Industry.
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AUTHORS
Ms. Khaing Thazin Min, in Myanmar University of Information Technology.
Mandalay City. She is a Lecturer working in the Faculty of Computer Systems and
Technology. Her received a master's degree in Computer Technology from Computer
University (Mandalay) in 2012. In addition, she attended teaching support courses run
by the university. Her current areas of interest include Wireless Sensor Networks
(WSN), Blockchain, IOT and Network Security. She is currently attending the ASEAN-
India Research Training Fellowships (6months) Program in VIT.
Dr. N. Jeyanthi, Professor in VIT, Vellore for School of Computer Science
Engineering and Information Sciences, received her PhD in Cloud Security from VIT,
Vellore. Her research work was funded by Department of Science and Technology,
Govt. of India. She has authored and co-authored over 70 research publications in peer-
reviewed reputed journals and 40 conference proceedings. She edited books for
Springer, Taylor & Francis and IGI. Book chapters were also added to her research
contribution. She has served as the program committee member of various international
conferences and reviewer for various international journals. She has been honoured by
VIT as an active researcher for five consecutive years. Her accomplishments have earned her the Dr.A P J
Abdul Kalam, Best Teacher and Researcher Award for 2022. Her current areas of interest include Quantum
Cryptography, Information Security, IoT, Cloud, and Big Data security.

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