Dao Thi-Kien, Chu Shu-Chuan, Nguyen Trong-The, Nguyen Trinh-Dong, Nguyen Vinh-Tiep
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Entropy (Basel). 2022 Jul 23;24(8):1018. doi: 10.3390/e24081018.
Node coverage is one of the crucial metrics for wireless sensor networks' (WSNs') quality of service, directly affecting the target monitoring area's monitoring capacity. Pursuit of the optimal node coverage encounters increasing difficulties because of the limited computational power of individual nodes, the scale of the network, and the operating environment's complexity and constant change. This paper proposes a solution to the optimal node coverage of unbalanced WSN distribution during random deployment based on an enhanced Archimedes optimization algorithm (EAOA). The best findings for network coverage from several sub-areas are combined using the EAOA. In order to address the shortcomings of the original Archimedes optimization algorithm (AOA) in handling complicated scenarios, we suggest an EAOA based on the AOA by adapting its equations with reverse learning and multidirection techniques. The obtained results from testing the benchmark function and the optimal WSN node coverage of the EAOA are compared with the other algorithms in the literature. The results show that the EAOA algorithm performs effectively, increasing the feasible range and convergence speed.
节点覆盖是无线传感器网络（WSNs）服务质量的关键指标之一，直接影响目标监测区域的监测能力。由于单个节点的计算能力有限、网络规模以及操作环境的复杂性和不断变化，追求最优节点覆盖面临着越来越大的困难。本文基于增强的阿基米德优化算法（EAOA），提出了一种在随机部署期间解决不平衡WSN分布的最优节点覆盖问题的解决方案。使用EAOA将几个子区域的网络覆盖最佳结果进行组合。为了解决原始阿基米德优化算法（AOA）在处理复杂场景方面的缺点，我们通过反向学习和多方向技术对其方程进行调整，提出了一种基于AOA的EAOA。将测试基准函数得到的结果以及EAOA的最优WSN节点覆盖与文献中的其他算法进行比较。结果表明，EAOA算法有效，增加了可行范围和收敛速度。
