Abstract: In the Internet of Things (IoT) era, energy efficiency in Wireless Sensor Networks (WSNs) is of utmost importance given the finite power resources of sensor nodes. An efficient Cluster Head (CH) selection greatly influences network performance and lifetime. This paper suggests a novel energy-efficient clustering protocol that hybridizes Whale Optimization Algorithm (WOA) and Artificial Ecosystem Optimization (AEO), called WOAAEO. It utilizes the exploration capabilities of AEO and the exploitation strengths of WOA in optimizing CH selection and balancing energy consumption and network efficiency. The proposed method is structured into two phases: CH selection using the WOAAEO algorithm and cluster formation based on Euclidean distance. The new method was modeled in MATLAB and compared with current algorithms. Results show that WOAAEO increases the network lifetime by a maximum of 24%, enhances the packet delivery rate by a maximum of 21%, and reduces energy consumption by a maximum of 35% compared to related algorithms. The results show that WOAAEO can be a suitable solution to help resolve energy-saving issues in WSNs and can thus be applied to IoT without any issues.

Shengnan BAI, Ningning LIU, Yongbing JI and Kecheng WANG, “WOAAEO: A Hybrid Whale Optimization and Artificial Ecosystem Optimization Algorithm for Energy-Efficient Clustering in Internet of Things-Enabled Wireless Sensor Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160465

@article{BAI2025,
title = {WOAAEO: A Hybrid Whale Optimization and Artificial Ecosystem Optimization Algorithm for Energy-Efficient Clustering in Internet of Things-Enabled Wireless Sensor Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160465},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160465},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Shengnan BAI and Ningning LIU and Yongbing JI and Kecheng WANG}
}