Social Group Optimization (SGO) for Clustering in Wireless Sensor Networks

Kalla, Neeharika; Parwekar, Pritee

doi:10.1007/978-981-10-7566-7_13

Cited by 3 publications

(1 citation statement)

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“…In [33], a new hierarchical adaptive energy-efficient clustering routing (HAECR) method is proposed for underwater data collection, which divides hierarchical regions according to the depth of sensor nodes, which can effectively reduce energy consumption and extend the network life, but the algorithm converges too slowly and does not take into account complex underwater biological factors. In [34], the authors proposed a new algorithm based on social group optimization (SGO) to reduce communication distances to reduce network energy consumption. In addition, the use of new optimization techniques to dynamically select the head node can significantly reduce the energy consumption of the network.…”

Section: Related Workmentioning

confidence: 99%

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Zhong,

Zhou,

Zhang

2023

Applied Sciences

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Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

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Section: Related Workmentioning

confidence: 99%