Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer

Cheng, Chao; Han, QingShan; Cheng, Guoli; Zhai, Shuying

doi:10.4108/eai.27-8-2020.2295028

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…In this research, the classic Low-energy adaptive clustering hierarchy (LEACH) energy model [10,24] is used to calculate network energy consumption and exhausted energy for all network nodes. The same first order wireless communication model was used by [25]. Following assumptions have been made:…”

Section: Energy Modelmentioning

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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Section: Energy Modelmentioning

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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“…In [14], for adapting to the real situation, the process energy use of the nodes would be limited, and a three-phase energy heterogeneous network model would be designed.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

Nabavi

Eraghi

Torkestani

2022

Wireless Communications and Mobile Computing

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With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.

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Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer

Cited by 2 publications

References 28 publications

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

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

Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

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