©2025JETIRJanuary2025, Volume12, Issue1 www.jetir.

org(ISSN-2349-5162)

SECURE ROUTING PROTOCOL DESIGNED

TO ENHANCE ENERGY EFFICIENCY IN WSN
APPLICATIONS
1
GuruprasadAM, 2Dr.RajalakshmiMC
1
Researchscholar, 2 Professors
1
Electronics and Communication Engineering,
1
VVIET, Mysore, India.

Abstract: This paper evaluates the energy efficiency of clustering-based communication in wireless sensor networks (WSNs)
compared to direct transmission. A network of 50 nodes, randomly deployed over a 100x100-meter area, is simulated with nodes
initialized with 2 Joules of energy. Nodes communicate data packets either through cluster heads or directly to a base station
located at the center. Cluster heads are dynamically chosen based on energy levels within a 20-meter radius. The simulation
iterates over 100 rounds, during which cluster formation, intra-cluster communication, and base station data transfer are modeled.
Energy consumption is calculated by a dissipation model considering transmission and amplification energies. A comparison is
drawn between total energy consumed in clustering and direct transmission. Results show significant energy savings with
clustering due to optimized intra-cluster communication and dynamic cluster head selection. The findings demonstrate clustering
as a viable method to enhance the longevity and energy efficiency of WSNs. A bar chart visually compares energy consumption
between the two approaches, highlighting the effectiveness of clustering in reducing total energy overheads.

Index Terms: WSN, Cluster head, Security, Energy efficiency

1. INTRODUION

Wireless Sensor Networks (WSNs) are critical in various applications such as environmental monitoring, healthcare, and industrial
automation. These high level networks consist of numerous resource-constrained sensor nodes that gather and transmit data to a
central node or base station. However, one of the most significant challenges in WSNs is energy efficiency, as sensor nodes are
typically powered by batteries with limited lifespan. Inefficient routing protocols can result in excessive energy consumption,
reducing the network's operational lifespan and limiting its effectiveness in real-world applications. Therefore, designing secure
and energy-efficient routing protocols is playing vital to prolonging network longevity while ensuring reliable data delivery.
This research introduces a secure routing protocol aimed at enhancing energy efficiency in WSN applications. The proposed
effective protocol optimizes energy consumption by selecting energy-aware routing paths and employing data aggregation
techniques to minimize redundant transmissions. In addition, it incorporates robust security mechanisms to safeguard the network
against potential threats such as data tampering, node impersonation, and denial-of-service attacks .By balancing energy efficiency
and security, the protocol ensures the reliability and sustainability of WSNs, making them more suitable for deployment in critical
and resource-intensive applications.

Figure1:Clustering and cluster-head election.

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2. LITERATURESURVEY

Dhivya et al. [1] Highlight that wireless sensor network (WSN) applications are utilized across various industries to monitor
factors such as pollution, temperature, and disaster management. Clustering is identified as the most effective method employed
in WSNs. The authors also conducted a survey on WSNs, categorizing them based on clustering properties.

Singh et al. [2] Discussed the physical factors, architecture, and advancements in WSN technology, providing a more detailed
examination of the types and applications of WSNs. And mainly deals with Energy Efficiency, Data Transmission and
Communication, Scalability Fault Tolerance Security and Privacy. And some energy efficient techniques such as LEACH,
AODV ,Directed Diffusion.

Mohamed et al. [3] Suggested that WSN applications should be implemented across diverse fields, noting that route selection and
energy overhead are critical for enhancing the longevity and efficiency of the network.

Yi et al. [4] This paper introduces Micro Electro Mechanical Systems (MEMS), and Wireless Sensor Networks (WSN). The
authors concluded by highlighting the key conditions necessary for the future development of WSNs. And uses the technique
Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN)

Anisi et al. [5] Assessed the role of WSNs in agriculture, emphasizing their potential to reduce energy consumption in farming
practices. Their review focused on the techniques of WSNs as they relate to precision agriculture.

Kaur et al. [6] Conducted a survey on WSN routing protocols, detailing the categorization and applications of these protocols for
future research. And they use Flat Routing hierarchical directing Location based Routing

AL-Mousawi and AL-Hassani et al. [7] In this paper, modern practical studies of the wireless sensor network in explosives
detection field have been taken into consideration and statistics are being conducted on these studies. Explosive detection
requires the use of special sensors and compatible to work with the wireless sensor network. This paper deals with three main
axes in wireless sensor systems: first axes is the scalability of wireless sensors in explosives detection technology. Second axes of
WSN explosives detection system is the communication and mobility of these networks and sensors. The latest axes discuss the
data security of the wireless sensor network used in the process of detecting explosives and the potential dangers that the system
can direct in terms of data security.

Ali et al. [8] Presented a survey on real-time applications of WSNs, noting their effectiveness in monitoring various factors such
as water levels, traffic, health, and temperature. The authors concluded that WSNs are particularly well-suited for monitoring
hard-to-reach areas, and they explains more about the Scalability, Reliability Responsiveness, Mobility, Power efficiency of
WSN and also made an Extensive survey in real-time applications of wireless sensor network deployment.

Rashid and Rehmani [9] Conducted comprehensive research on wireless sensor networks (WSNs) in urban environments,
examining their benefits, drawbacks, applications, and challenges.

Abdollazadeh and Navimipour [10] The proposed a method for investigating, analyzing, and determining sensor deployment
strategies.They categorized deployment issues based on four different techniques and conditions, discussing the advantages,
disadvantages, and challenges associated with sensor deployment. The authors also reviewed traditional sensor applications
within WSNs, detailing the properties and architecture of these networks. To conclude their discussion, they highlighted several
challenges related to sensor functionality.

Belfkih et al. [11] Introduced a sensor database designed to manage the vast amounts of data generated by the increasing number
of deployed sensors across the network, addressing various research issues related to the database.

Shafiq et al. [12] Recommended conducting an assessment of the energy efficiency of wireless sensor networks (WSNs). Their
evaluation encompasses various factors, including power efficiency, threshold sensitivity, and low-energy methods, among
others. The review also addresses the limitations and challenges associated with current WSN applications, revealing that energy
consumption is the main issue affecting WSN performance.

Amutha et al. [13] Conducted a comprehensive analysis of WSN categorization, organizing the categories based on deployment
methods, coverage, types of sensors, energy efficiency, and sensing models.

Sharma et al. [14] Proposed the use of machine learning techniques for wireless sensor networks (WSNs) in smart city
applications, and Network Optimization method by using this Data Analysis and Predictive Capabilities of sensor network is
enhanced. Data Privacy and Security Concerns, Maintenance and Adaptation Integration Complexity.highlighting a comparison
among supervised learning (61%), unsupervised learning (27%), and reinforcement learning (27%), with supervised learning
being the most prevalent in smart city contexts.

Temene et al. [15] In this work we review and classify algorithms that introduce the characteristic of mobility in WSNs. We
consider WSNs to be both a subset and a predecessor to IoT, and thus, consider that existing mobility solutions can be adapted
for use in IoT. Finally, open problems and future directions are discussed that include wireless power transfer, network fault
detection, and real-world/testbed evaluation of algorithms.
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Paruvathavardhini and Sargunam [16] Introduced an advanced technique called QIEAC-CSSBO (quantized indexive energy-
aware clustering-based combinatorial stochastic sampled bat optimization) aimed at enhancing both energy efficiency and secure
routing within WSNs. This method employs the quantized Schutz indexive Linde–Buzo–Gray technique for clustering sensor
nodes, while the combinatorial stochastic sampled Prevosti’s bat optimization algorithm is utilized to identify safe and efficient
paths.

Nagarajan and Kannadhasan [17] In this paper The Blended model for Network Intrusion Detection, an unique strategy that
makes use of machine learning, is presented in this research. It is intended to provide excellent accuracy when used with an
ensemble learning algorithm. CIC-IDS2018, a dataset made available by the Canadian Institute of Cyber Security, was the one
utilised in this study. This study intends to determine high detection accuracy of 95.49 and 95.39 F1 score by reducing the data
dimension using SMOTE (Synthetic Minority Oversampling Technique) in conjunction with feature selection and Ensemble
Random Forest algorithm.

Paruvathavardhini et al. [18] Proposed a security framework for clustered routing protocols, which helps minimize energy
consumption by preventing all nodes from being active simultaneously. However, if a single cluster head is consistently used,
that node may deplete its energy more quickly and become unable to communicate. To address this, a new method for selecting
cluster heads was developed. Clustering also helps alleviate the burden on gateway nodes in single-tier networks, making it a
more effective approach. In real-time monitoring applications, WSNs are essential for sensing, processing, and forwarding data to
a sink node. When the sink node is nearby, communication can occur directly in a single hop. However, due to the extensive
nature of WSNs, which can consist of thousands of nodes, routing becomes complex when the sink is far from the source, often
requiring multiple hops. This multihop communication can lead to data loss and delays if a faulty route is chosen, resulting in
resource wastage and accelerated node depletion. Therefore, secure routing is feasible only when supported by an optimized
protocol to prevent interruptions. WSNs are considered an early form and subset of the Internet of Things. The authors also
discussed potential advancements and challenges within the network, including wireless power transfer and network failure
detection.

Hosseinzadeh et al. [19] They presented the CTRF cluster-based trusted routing algorithm, which employs a fire hawk optimizer
to improve network security while considering the limited energy of nodes. This approach includes a weighted trust mechanism
(WTM) based on interactions among sensor nodes, highlighting the complexity of the route construction process.

Dass et al. [20] a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been
proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and
by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main
advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-
delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay.

K. Saleem et al. [21] This paper discussed about the Biological inspired self-organized secure autonomous routing protocol
(BIOSARP) BIOSARP has been designed to reduce the broadcast and packet overhead in order to minimize the delay, packet
loss, and power consumption in the WSN. But the problem behind this is Complexity in the implementation and maintenance of
the protocol security measures and self-organizing processes could lead to increased resource consumption.

Khot and Naik [22] Proposed a secure routing strategy that integrates water wave optimization (WWO) with particle swarm
optimization (PSO), resulting in improved node longevity and a higher number of active nodes compared to other methods.

Rajalakshmi M C and Gnana Prakash A P [23] This paper presents a novel routing protocol that performs a cost effective,
reliable and robust routing mechanism in wireless sensor network with its highly compatibility with in-network data aggregation
mechanism. A simulation study is performed Matlab, where along with proposed routing algorithm, two more conventional
algorithm is chosen to perform the performance comparative analysis.

Rajalakshmi M C and Gnana Prakash A P [24],This paper proposed study introduces a mathematical modeling of energy
efficiency technique considering this aspect by i) formulating empirical representation of energy depleted at sensor mote during
data aggregation, ii) framing up condition for optimizing the network lifetime for large scale WSN, and iii) rate of data
transmission. The proposed technique also discusses about the conditions required to select cluster head to ensure that energy is
conserved maximally during and after selection of cluster head. The simulation being conducted with 500-1000 sensor motes
shows that proposed system have optimal energy retention, better energy optimization rate, and higher throughput as compared to
the most frequently adopted energy efficient hierarchical routing protocol.

Rajalakshmi M C [25], This paper discusses some of the standard techniques that have evolved in the past with a claim of
efficient power preservation techniques. The paper has also discussed some of the recent techniques identified that has the
potentials of saving the energy depletion.

Rajalakshmi M C and Gnana Prakash A P [26], This paper introduces MOMEE i.e. Manifold Optimized Modeling of Energy
Efficiency that offers novel clustering as well as novel energy optimized routing strategy. The proposed system uses analytical
modeling methodology and is found to offer better resiliency against traffic bottleneck condition. The study outcome of MOMEE
exhibits higher number of alive nodes, lower number of dead nodes, good residual energy, and better throughput as compared to
existing energy efficient routing approaches in wireless sensor network.

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Rajalakshmi M C and Gnana Prakash A P [27], The paper presents a technique called as Mobility-Enabled Multi Level
Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique
enables selection of aggregator node based on multiple optimization attribute which gives better decision capability to the
clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of
minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node.

Rajalakshmi M C and Gnana Prakash A P [28],The paper has discussed about the various real-time constraint and limitation of
the sensor motes in large scale WSN and eventually performed a problem formulation of the study in most simple and yet
effective manner. in order to increase the more challenging circumstances, a bit of mobility environment of the base station is
also introduced for which computational model of retention of residual energy of the cumulative nodes in the network becomes
one of the significant asset of this manuscript.

Zhou et al. [29] This paper proposed the new protocol Energy-Aware LEACH++ Protocol for Jamming-Resilient WSNs which
improves the LEACH energy efficiency by 18% and provides resistance towards attacks but Vulnerable to advanced jamming
attacks.

Chen et al. [30] In this paper, a routing protocol is proposed that will take in consideration the energy consumption of the
heterogeneous devices. A SDN controller is also introduced in the network that serves as a centralized manager providing a
secure network by denying access to selfish nodes that are present in the network. Centralized threat detection reduced energy
waste by 20%.the technique used is SDN-Based Secure Routing but the drawback is Single point of failure at the SDN controller
may affect the overall performance of the routing protocol.

Almeida et al. [31] In this paper, a routing protocol is proposed that is Bio-Inspired Ant Colony Optimization for Energy-
Balanced WSN Routing which will Balanced energy usage using ant colony optimization and increase a network life up to
30%.but disadvantage is Slow convergence in large-scale networks.

Fursan Thabit et al. [32] This paper presents a novel, effective lightweight homomorphic cryptographic algorithm which contains
two layers of encryption. The first layer uses the new effective, light-weight cryptographic algorithm and the second layer
multiplicative homomorphic schemes considered for improving security data in cloud computing. This approach offers both
symmetric and asymmetric cryptography features. The proposed approach’s performance is evaluated using a variety of metrics,
including memory, computational time and (key sensitivity), statistical analysis, image histogram, and entropy change analysis.
The proposed algorithm’s experimental findings showed a high level of security and an apparent improvement in encryption
execution time, memory usage, and throughput. When compared to the cryptographic systems widely used in cloud computing.
Garcia et al. [33] In this paper they use the technique Trust-Based Geographic Routing to Mitigate Black hole Attacks in WSNs
with 85% detection accuracy. But GPS dependency raises energy costs.

Patel et al. [34] In this paper they design the new protocol that is Hybrid AES-ECC Authentication for Energy-Efficient WSN
Routing which Achieved 90% secure packet delivery with 25% energy savings but the disadvantage is increased latency due to
cryptographic operations.

Ivanova et al.[35] In this paper they mainly focus on the cryptographically methods to increases the security by designing the
routing protocol Post-Quantum Cryptography for Secure Routing in Resource-Constrained WSNs and by using Quantum-
resistant key exchange will reduce 20% utilization of energy the limitation is interoperability with legacy WSN hardware.

Balakumar Muniandi et al. [36] This paper presents a comprehensive review of AI-driven energy-efficient routing protocols
tailored specifically for WSNs. It delves into the various methodologies of AI, including machine learning, evolutionary
algorithms, deep learning, and reinforcement learning, and their integration into routing protocols to achieve optimal energy
utilization. Machine learning-based approaches leverage historical data to predict traffic patterns and dynamically adjust routing
decisions, thereby optimizing energy consumption. Evolutionary algorithms offer a nature-inspired optimization paradigm,
evolving routing strategies over time to adapt to changing network conditions. Deep learning techniques enable the extraction of
intricate features from sensor data, facilitating more informed routing decisions. Reinforcement learning empowers sensor nodes
to autonomously learn and adapt their routing strategies based on feedback from the environment.

Zhang & Lee et al. [37] This paper proposed the new routing protocol that is AI-Driven Dynamic Clustering for Energy-Aware
WSN Routing which used clustering methods in turn improved network lifetime by 40% with adaptive cluster head selection.
There is complexity in training phase which requires centralized processing.

J. Panda et al. [38],This study proposes secure attack localization and detection in IoT-WSNs to improve security and service
delivery. The technique used block chain-based cascade encryption and trust evaluation in a hierarchical design to generate block
chain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and
feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing
malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves
data security and transmission by merging raw device data and placing malicious threats in the block chain. Malicious nodes are
classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, K-
means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing
the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for
multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance
and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.

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Smith et al. [39] This paper proposed the new routing protocol Federated Learning and Block chain for Energy-Efficient Secure
Routing in WSNs which will reduce energy consumption by 35% while preventing Sybil attacks. But the disadvantage is High
computational overhead for resource-constrained nodes.

Rabindra Kumar Shial et al. [40] They design a routing strategy that is effective and energy-efficient to address the unfairness of
high traffic loads on WSNs used by Internet of Things (IoT) applications. The suggested protocol chooses the best path based on
reliability, lifespan, and next-hop node traffic. For stiff simulations, MATLAB R2015a was utilized. The suggested protocol's
Hybrid Energy Efficiency Routing Protocol for Optimal Path in the Internet of Things-Based Sensor Networks (HEERPOP)
performance is also compared to other existing modern protocols. The proposed protocol gives better outcomes in terms of
energy consumption, packet delivery ratio, end-to-end latency, and network longevity. The disadvantage is Random Cluster Head
Selection, Limited Network Lifetime Security Vulnerabilities Communication Overhead Assumption of Uniform Node
Distribution.

3. PROBLEMSTATEMENT

The increasing proliferation of devices within wireless sensor networks (WSN) has led to significant security vulnerabilities and
challenges in energy efficiency. As these networks expand, the risk of data theft and malicious activities escalates, compromising
legitimate data delivery and degrading overall network performance. Existing security measures have proven insufficient ,allowing
rogue nodes to infiltrate the network, which results in increased latency, data loss, and a decline in service quality .Additionally,
the energy consumption of sensor nodes is a critical concern, as inefficient routing and the presence of malicious nodes can lead to
rapid battery depletion, ultimately shortening the lifespan of the network. To address these pressing issues, this study proposes a
Machine Learning-Based Secured Routing Protocol (MLSRP) designed to enhance both security and energy efficiency in WSNs.
By implementing a clustered network approach and utilizing a multicriteria decision-making model for effective data routing and
cluster management, the proposed protocol aims to isolate malicious nodes and improve the overall performance of the network.

4. OBJECTIVES
1. To design and implement the Cluster-Based Secured Routing Protocol (CLSRP) specifically tailored for IoT-based
wireless sensor networks (WSNs).
2. To improve the energy economy of WSN applications. The MLSRP (CL-Based Secured Routing Protocol) will
incorporate energy-efficient routing strategies that minimize energy consumption during data transmission. This will
involve the use of a multicriteria decision-making approach to select optimal paths for data routing, ensuring that the
energy resources of the IoT nodes are utilized effectively. By focusing on energy efficiency, the protocol aims to extend
the operational lifetime of the network and reduce the frequency of node replacements.
3. To establish a comprehensive multi-layered architecture for the CLSRP, consisting of control, equipment, service, and
node layers.
4. TodevelopmechanismswithintheCLSRPtoeffectivelydetectandisolatemaliciousnodeswithinthenetwork.

5. PROPOSEDMETHODOLOGY
This study simulates a wireless sensor network (WSN) to compare energy consumption between clustering-based communication
and direct transmission. The methodology involves the following steps:
1. Network Initialization:
A WSN with 50 nodes is deployed randomly within a100x100-meter area.
Each node is initialized with 2Joules of energy, and a base station is located at the center(50, 50).
2. Clustering and Cluster Head Selection:
Nodes within a 20-meter radius form clusters.
The node with the highest energy within each cluster is designated as the clusterhead.
3. Communication:
Cluster heads transmit aggregated data directly to the base station using an energy
dissipation model. Non-cluster-head nodes send data to their respective cluster heads.
4. Direct Transmission Comparison
For comparison, energy consumption is also calculated for direct transmission from all nodes to the base station.
5. Simulation Loop:
The simulation runs for 100 rounds or until all nodes are dead.
6. Energy usage and the number of alive nodes are tracked in each round.
Cluster heads are reselected at the start of each round to ensure balanced energy consumption.
7. Energy Savings Calculation:
Total energy consumed using clustering is compared to direct transmission to evaluate energy efficiency.
8. Visualization:
A bar chart is generated to illustrate energy consumption with and without clustering.

6. RESULTSANDDISCUSSIONS
The simulation evaluates energy efficiency in a sensor network with and without clustering. Nodes are randomly distributed, and
clustering is implemented where nodes communicate with cluster heads, which then transmit data to a central base station.
Clustering reduces energy consumption by minimizing the transmission distance for most nodes. Over 100 rounds, the energy
consumption with clustering is consistently lower compared to direct transmission. Cluster heads, selected based on energy
availability, perform more work, but overall network efficiency is improved, prolonging the lifespan of the nodes. The results
show significant energy savings due to clustering, emphasizing its effectiveness in reducing energy dissipation in wireless sensor
networks. The bar plot illustrates the comparison, confirming the advantage of clustering for energy optimization.
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Figure2:Initial WSN layout Final network state

Figure3: Comparison of energy

Round01:Alivenodes=50 Round51:Alivenodes=50
Round02:Alivenodes=50 Round52:Alivenodes=50
Round03:Alivenodes= 50 Round53:Alivenodes=50
Round04:Alivenodes=50 Round54:Alivenodes=50
Round05:Alivenodes=50 Round55:Alivenodes=50
Round06:Alivenodes=50 Round56:Alivenodes=50
Round07:Alivenodes=50 Round57:Alivenodes=50
Round08:Alivenodes=50 Round58:Alivenodes=50
Round09:Alivenodes=50 Round59:Alivenodes=50
Round10:Alivenodes=50 Round60:Alivenodes=50
Round11:Alivenodes=50 Round61:Alivenodes=50
Round12:Alivenodes=50 Round62:Alivenodes=50
Round13:Alivenodes=50 Round63:Alivenodes=50
Round14:Alivenodes=50 Round64:Alivenodes=50
Round15:Alivenodes=50 Round65:Alivenodes=50
Round16:Alivenodes=50 Round66:Alivenodes=50
Round17:Alivenodes=50 Round67:Alivenodes=50
Round18:Alivenodes=50 Round68:Alivenodes=50
Round19:Alivenodes=50 Round69:Alivenodes=50
Round20:Alivenodes=50 Round70:Alivenodes=50
Round21:Alivenodes=50 Round71:Alivenodes= 50
Round22:Alivenodes=50 Round72:Alivenodes=50
Round23:Alivenodes=50 Round73:Alivenodes=50
Round24:Alivenodes=50 Round74:Alivenodes=50
Round25:Alivenodes=50 Round75:Alivenodes=50
Round26:Alivenodes=50 Round76:Alivenodes=50
Round27:Alivenodes=50 Round77:Alivenodes=50
Round28:Alivenodes=50 Round78:Alivenodes=50

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Round29:Alivenodes=50 Round79:Alivenodes=50
Round30:Alivenodes=50 Round80:Alivenodes=50
Round31:Alivenodes=50 Round81:Alivenodes=50
Round32:Alivenodes=50 Round82:Alivenodes=50
Round33:Alivenodes=50 Round83:Alivenodes=50
Round34:Alivenodes=50 Round84:Alivenodes=50
Round35:Alivenodes=50 Round85:Alivenodes=50
Round36:Alivenodes=50 Round86:Alivenodes=50
Round37:Alivenodes=50 Round87:Alivenodes=50
Round38:Alivenodes=50 Round88:Alivenodes=50
Round39:Alivenodes=50 Round89:Alivenodes=50
Round40:Alivenodes= 50 Round90:Alivenodes=50
Round41:Alivenodes=50 Round91:Alivenodes=50
Round42:Alivenodes=50 Round92:Alivenodes=50
Round43:Alivenodes=50 Round93:Alivenodes=50
Round44:Alivenodes=50 Round94:Alivenodes=50
Round45:Alivenodes=50 Round95:Alivenodes=50
Round46:Alivenodes=50 Round96:Alivenodes=50
Round47:Alivenodes=50 Round97:Alivenodes=50
Round48:Alivenodes=50 Round98:Alivenodes=50
Round49:Alivenodes=50 Round99:Alivenodes=50
Round50:Alivenodes=50 Round100:Alivenodes= 50
Total energy saved:0.25029

Node01sent secure data with hash: 191

Node02 sent secure data with hash: 224
Node03 sent secure data with hash: 222
Node04 sent secure data with hash: 193
Node05 sent secure data with hash: 211
Node06 sent secure data with hash: 190
Node07 sent secure data with hash: 219
Node08 sent secure data with hash: 214
Node09 sent secure data with hash: 163
Node10 sent secure data with hash: 222
Node11 sent secure data with hash: 244
Node12 sent secure data with hash: 187
Node13 sent secure data with hash: 211
Node14 sent secure data with hash: 225
Node15 sent secure data with hash: 220
Node16 sent secure data with hash: 174
Node17 sent secure data with hash: 158
Node18 sent secure data with hash: 217
Node19 sent secure data with hash: 209
Node20 sent secure data with hash: 231
Node21 sent secure data with hash: 158
Node22 sent secure data with hash: 235
Node23 sent secure data with hash: 180
Node24 sent secure data with hash: 219
Node25 sent secure data with hash: 231
Node26 sent secure data with hash: 230
Node27 sent secure data with hash: 171
Node28 sent secure data with hash:32
Node29 sent secure data with hash: 224
Node30 sent secure data with hash: 245
Node 31 sent secure data with hash: 230
Node32 sent secure data with hash: 203
Node33 sent secure data with hash: 198
Node34 sent secure data with hash: 169
Node35 sent secure data with hash: 23
Node36 sent secure data with hash: 208
Node37 sent secure data with hash: 170
Node38 sent secure data with hash: 43
Node 39 sent secure data with hash: 165
Node40 sent secure data with hash: 237

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Node41 sent secure data with hash: 234
Node42 sent secure data with hash: 214
Node43 sent secure data with hash: 190
Node44 sent secure data with hash: 226
Node45 sent secure data with hash: 169
Node46 sent secure data with hash: 244
Node47 sent secure data with hash: 206
Node48sent secure data with hash: 235
Node49 sent secure data with hash: 224
Node50 sent secure data with hash: 221

7. CONCLUSION
The simulation demonstrates that clustering significantly improved the energy efficiency of wireless sensor networks by reducing
the energy required for data transmission. By leveraging localized communication within clusters and assigning cluster heads to
relay data to the base station, the total energy consumption is minimized compared to direct transmission methods. The clustering
approach ensures better energy distribution among nodes, prolonging their operational lifespan. In excess of multiple simulation
rounds, the energy savings achieved highlight the effectiveness of clustering in optimizing network energy usage, making it a
superior method for sustainable sensor network design.

8. REFERENCES
1. S. Dhiviya, A. Sariga, and P. Sujatha, ―Survey on WSN using clustering,‖ in 2017 Second International Conference on
Recent Trends and Challenges in Computational Models (ICRTCCM), pp. 121–125, IEEE, Tindivanam, India, Febru-ary
2017.
2. M. K. Singh, S. I. Amin, S. A. Imam, V. K. Sachan, and A. Choudhary, ―A survey of wireless sensor network and its types,‖
in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN),pp.
326–330, IEEE, Greater Noida, India, October 2018.
3. R. E. Mohamed, A. I. Saleh, M. Abdelrazzak, and A. S. Samra, ―Survey on wireless sensor network applications and
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