WIRELESS SENSOR NETWORKS BASED ON IMPROVED DUNG BEETLE ALGORITHM
Keywords:
Dung beetle optimization, Wireless sensor network coverage, Logistic chaotic initialization, Levy flight strategy, Dynamic nonlinear convergence factorAbstract
To address the limitations of traditional Dung Beetle Optimization (DBO) in the wireless sensor network coverage problem, specifically like low convergence speed and susceptibility to local optima, this paper proposes an optimization scheme based on the Improved DBO algorithm (IDBO). The algorithm combines three key strategies: first, a Logistic chaos initialization strategy is used to generate a more optimal initial solution; second, a Levy flight strategy is introduced to enhance the global search capability; and finally, the search process is further optimized by using a dynamic nonlinear convergence factor to adaptively adjust the search step size. With the above improvements, the algorithm is significantly improved in global search performance. Experiments on the CEC2005 benchmark suite show that IDBO outperforms similar algorithms and approaches the global optimum. Finally, at last, the proposed IDBO algorithm is applied to the wireless sensor network coverage optimization problem for simulation experiments. The simulation results show that the improved IDBO algorithm improves the coverage of the network nodes by 4.9% compared to the basic DBO algorithm and enhances the overall performance of the network with good practicality, stability and robustness.References
[1] Li X, Li B, Li Y. Per-connection vs. service subscription: A study on service charging model of IoT platform empowering manufacturers to upgrade product intelligence. Nankai Management Review, 2024: 1–27.
[2] Zhang J. Research on data aggregation algorithm for wireless sensor networks based on node heterogeneity. Hebei: Hebei University, 2024.
[3] Chen G, Wan W. Trust assessment and node selection strategy based on distributed cloud node task collaboration. Guangdong Communication Technology, 2023, 43(12): 45–50.
[4] Zhu J. Research on node layout, localization, and mobile node path planning problems in WSN. Shenyang: Northeastern University, 2010.
[5] Huang Q, Lai C. Research on communication route planning algorithm for WSNs based on fuzzy affiliation optimization algorithm. Automation Technology and Application, 2024: 1–8.
[6] Song J, Hu Y M, Luo Y B. Comparative analysis of swarm intelligent optimization algorithms based on WSN network coverage optimization problem. Journal of Dali University, 2024, 9(12): 65–73.
[7] Dong H, Pan F. Constant tension fuzzy control of lithium battery winding based on genetic algorithm. Automation and Instrumentation, 2025, 40(2): 43–48.
[8] Wei S, Jia H, Cao J, et al. Prediction of heat transfer performance of buried pipe based on particle swarm optimization algorithm. District Heating, 2025(1): 107–116.
[9] Wang J, et al. An improved bat algorithm for node localization in wireless sensor networks. Applied Soft Computing, 2020, 89: 106156.
[10] Li X, et al. Energy-efficient cluster head selection in WSNs using differential evolution. Engineering Applications of Artificial Intelligence, 2021, 97: 104041.
[11] Kumar S, Singh P. TLBO-based energy balancing in wireless sensor networks for IoT applications. Ad Hoc Networks, 2022, 135: 102952.
[12] Chen H, et al. Multi-objective sparrow search algorithm for coverage and energy optimization in WSNs. IEEE Sensors Journal, 2023, 23(5): 5123–5135.
[13] Zhang L, et al. SCA-based Q-learning routing protocol for energy-efficient wireless sensor networks. Computer Networks, 2021, 194: 108133.
[14] Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82–102.
[15] Kennedy J. Particle swarm optimization// Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia). IEEE, 1995, 4: 1942–1948.
[16] Goldberg D, Holland J. Genetic algorithms and machine learning. Machine Learning, 1988, 3(2): 95–99.
[17] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46–61.
[18] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95: 51–67.
[19] Wang W, Tian W, Xu D, et al. Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization. Advances in Engineering Software, 2024, 195: 103694.
[20] Xue J, Shen B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 2023, 79: 7305–7336.
[21] Liu J S, Li W X, Li Y. LWMEO: An efficient equilibrium optimizer for complex functions and engineering design problems. Expert Systems with Applications, 2022, 198: 116828.
[22] Wang H, Zhang X, Lu H. Sensor coverage optimization strategy based on geometric coverage algorithm. Computer Applications Research, 2017, 34(8): 2478–2482.