Research on Coverage Optimization in a WSN Based on an Improved COOT Bird Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 9; p. 3383
• Main Authors: Huang, Yihui, Zhang, Jing, Wei, Wei, Qin, Tao, Fan, Yuancheng, Luo, Xuemei, Yang, Jing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.04.2022; MDPI
• Subjects: Algorithms; Animal behavior; Benchmarking; chaotic tent map; Communication; Computer Simulation; COOT bird optimization algorithm; coverage optimization; Data Collection; Integer programming; Linear programming; Lévy flight; Mathematical optimization; opposition-based learning; Optimization algorithms; Query expansion; Sensors; Wireless networks; Wireless sensor networks; Wireless Technology - instrumentation; coverage optimization; chaotic tent map; opposition-based learning; wireless sensor networks; COOT bird optimization algorithm; Lévy flight
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22093383

To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems.