Open AccessElite Niche Particle Swarm Optimization for Energy Clustering in Aeronautical Wireless Sensor Network
Author(s) -
Min Xu,
Jie Zhou,
Rui Yang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/926/1/012024
Subject(s) - cluster analysis , particle swarm optimization , computer science , wireless sensor network , mathematical optimization , robustness (evolution) , energy consumption , simulated annealing , distributed computing , algorithm , engineering , mathematics , artificial intelligence , computer network , electrical engineering , biochemistry , chemistry , gene
Recently, a wide variety of applications of aeronautical wireless sensor networks (AWSNs) make the clustering algorithm a paramount issue and bring profound changes to Internet of Things in AWSN-based applications. In AWSN, an energy-clustering algorithm is very significant to reduce the total energy cost of sensing data and solve the energy balance problem in AWSN. However, it is a challenging issue in AWSNs to have a low lifetime and energy utilization rate. In order to achieve a low energy consumption and further improve the lifetime of AWSNs, in this paper, we propose an elite niche particle swarm optimization (ENPSO), which combine the elite selection and niche sharing mechanism to develop the algorithm’s convergence rate and robustness. In the simulation, we compare the energy cost optimized by ENPSO with that optimized by grey wolf optimization (GWO) and simulated annealing (SA). Simulation results demonstrate that the energy cost optimized by ENPSO is 9.63% and 19.54% smaller than GWO and SA when the number of nodes is 100 with 10% cluster heads. It obviously shows that the performance of proposed ENPSO is better than other two algorithms and have a faster convergent rate and better robustness.