Squirrel Search Optimization-Based Cluster Head Selection Technique for Prolonging Lifetime in WSN’s

Arunachalam, N.; Shanmugasundaram, G.; Arvind, R. Varun

doi:10.1007/s11277-021-08843-z

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…Arunachalam et al [ 26 ] adapted the squirrel search algorithm (SSO-CHST) to extend the lifetime in the sensor network by using the gliding factor to ensure optimal cluster heads in WSN. The fitness function was computed to determine the CH and CM.…”

Section: Related Workmentioning

confidence: 99%

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Somula,

Cho,

Mohanta

2024

Sensors

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The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network’s lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

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Section: Related Workmentioning

confidence: 99%

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Somula,

Cho,

Mohanta

2024

Sensors

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“…In the year 2020, Prahadeeshwaran and Priscilla proposed a hybrid elephant optimization algorithm called NIUS-HEHOA [21] to extend the lifespan of the network by selection energy balanced cluster heads. In the year 2021, Arunachalam et al [22] introduced Squirrel Search Optimization-based Cluster Head Selection Technique (SSO-CHST) was presented for enhancing sensor network lifetime by using a gliding factor to determine cluster head selection during data aggregation and dissemination. The sensor node with the lowest fitness value was the cluster member.…”

Section: Related Workmentioning

confidence: 99%

A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm

Kusla¹,

Brar²

2023

EAI Endorsed Scal Inf Syst

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INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.

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“…This model has overcome the control overheads and maximized the energy efficiency while comparing to the conventional techniques. In 2020, Arunachalam et al [2] have implemented a new cluster head selection technique by SSO algorithm to prolong the network lifetime through a gliding factor with the procedure of data dissemination and aggregation. The fitness value of each sensor node was estimated and then, they have arranged in ascending order, where the cluster member was selected through identifying the node with lower fitness value.…”

Section: Related Workmentioning

confidence: 99%

“…However, this model does not consider the factors like scalability and data aggregation. SSO [2] has minimized the normalized energy consumption and has reported improved network lifetime and superior throughput. On the other hand, it suffers from classifying the "nodes with worst fitness from the nodes with best fitness in the network".…”

Section: Problem Statementmentioning

confidence: 99%

Deep Neural Network With Meta-Heuristic Based Trust Aware Cluster Head Selection in WSN

Reddy¹,

Narsimha²

2022

INDJCSE

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The advanced progressions in Wireless Sensor Network (WSN) made this network an effective one in a huge range of applications. Though, the WSN environment suffers from security and energy complexities. WSN has several benefits and still, it has a few challenges. These complexities help the attackers for analyzing the network security and then, they may destroy entire networks. Hence, this work addresses the energy and security issue and adopts the deep learning and meta-heuristic-based trust-aware cluster head selection protocol in WSN. Here, Whale Optimization Algorithm (WOA) is used to select the optimal cluster head using the multi-objective function using constraints like the distance, energy, delay, and trust of nodes. Here, the security management in terms of node trust is determined by the artificial intelligent model termed Deep Neural Network (DNN) for maintaining the security in routing. Through the performance analysis, the performance evaluation has shown that the designed architecture offers reliable and feasible performance in WSN.

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Squirrel Search Optimization-Based Cluster Head Selection Technique for Prolonging Lifetime in WSN’s

Cited by 9 publications

References 27 publications

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm

Deep Neural Network With Meta-Heuristic Based Trust Aware Cluster Head Selection in WSN

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