Enhancing Whale Optimization Algorithm with Levy Flight for coverage optimization in wireless sensor networks

2021 ◽  
Vol 94 ◽  
pp. 107359
Author(s):  
R. Deepa ◽  
Revathi Venkataraman
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Lévy Flight ◽  
Levy Flight ◽  
Coverage Optimization ◽  
Whale Optimization

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

2019 ◽  
Vol 72 (2) ◽  
pp. 243-259 ◽  
Author(s):  
Mohammed M. Ahmed ◽  
Essam H. Houssein ◽  
Aboul Ella Hassanien ◽  
Ayman Taha ◽  
Ehab Hassanien
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Multi Objective ◽  
Whale Optimization

scholarly journals Solving Point Coverage Problem in Wireless Sensor Networks Using Whale Optimization Algorithm

2021 ◽  
Author(s):  
Mahnaz Toloueiashtian ◽  
Mehdi Golsorkhtabaramiri ◽  
Seyed Yaser Bozorgi Rad
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Energy Use ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Dynamic Power Management ◽  
Coverage Problem ◽  
Whale Optimization ◽  
Point Coverage

Todays, dynamic power management methods that decrease the energy use of sensor networks after their design and deployment are of paramount importance. In Wireless Sensor Networks (WSN), coverage and detection quality are one aspect of service quality and power consumption reduction aspect. The aim of the coverage problem is to monitor at least one node at each point in the targeted area and is divided into three categories: border, area, and point coverage. In point coverage, which is our interest, the problem is to cover specific points of the environment scattered on the surface of the environment; their position is decided on and called the goal. In this paper, a new metaheuristic algorithm based on Whale Optimization Algorithm (WOA) is proposed. The proposed algorithm tries to find the Best Solution (BS) based on three operations exploration, spiral attack, and siege attack. Several scenarios, including medium, hard and complex problems, are designed to evaluate the proposed technique, and it is compared to Genetic Algorithm (GA) and Ant Colony Optimization (ACO) based on time complexity criteria in providing a suitable coverage, network lifetime, energy consumption. The simulation results show that the proposed algorithm performs better than the compared ones in most scenarios.

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