Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm

Abstract

There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research. INDEX TERMS Feedback, K-means clustering, artificial fish swarm algorithm, network coverage, algorithm optimization.

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

“…In order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a WSN coverage optimization method based on an improved artificial fish swarm algorithm (AFSA) was proposed. 21 To monitor the interest field and obtain the valid data, this article proposes a WSN coverage optimization model based on improved whale algorithm, and the proposed algorithm could effectively improve the coverage of nodes in WSNs and optimize the network performance. 22 As the problems encountered in actual engineering are complex and changeable, there are many constraints.…”

“…The parameter l in formula ( 10) is a random number uniformly distributed between [21,1], and step g represents the attack step length of the wolves launching a siege. Assuming that the value range of the rth variable to be optimized is [min r , max r ], the wandering step length step t of the scout wolf, the step length step m of the fierce wolf rush, and the step length step g of the wolf attack in the same dimension have the following relationship…”

A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

“…A wireless sensor network node optimal coverage method based on improved genetic algorithm and binary ant colony algorithm was proposed in [18], the binary code expected a low intelligence of each ant, and each path corresponded to a comparatively small storage space, thus considerably improving the efficiency of computation; the proposed algorithm had a high coverage rate, thus prolonging the network lifetime efficiently. In [19], the authors proposed an artificial fish swarm algorithm WSNs coverage optimization algorithm, which had achieved better optimization results in the coverage optimization of WSNs and improved the coverage of the network, but the node coverage redundancy is higher. In [20], the authors proposed an enhanced deployment algorithm based on artificial bee colony (ABC).…”

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

“…They are designed on the basis of cognitive behaviour of certain biologically inspired entity e.g., ant, honeybee, firefly, frog, fish, cat, dolphin, etc. The studies that has used swarm intelligence linking with energy efficiency are as follows: Gray-wolf optimization (Arafat et al [91]), Bat algorithm (Cao et al [92]), flocking control scheme using swarm intelligence (Dai et al [93]), firefly mating optimization (Faheem et al [94]), fish algorithm with k-means clustering (Feng et al [95]), multi-swarm optimization (Hasan et al [96]), Harris' Hawk optimization (Houssein et al [97]), particle swarm optimization (Mukherjee et al [98]), Chicken swarm optimization (Osamy et al [99]), reinforcement learning with swarm intelligence (Wei et al [100]). However, different approaches have their own structure of working which is implemented on WSN on different targets of optimization towards energy efficiency.…”