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| Title | Sho-Ch: Spotted Hyena Optimization For Cluster Head Selection To Optimize Energy In Wireless Sensor Network |
|---|---|
| ID_Doc | 48655 |
| Authors | Sharma N.; Gupta V.; Johri P.; Elngar A.A. |
| Year | 2025 |
| Published | Peer-to-Peer Networking and Applications, 18, 3 |
| DOI | http://dx.doi.org/10.1007/s12083-025-01949-2 |
| Abstract | Wireless Sensor Networks (WSNs) are essential in various applications such as environmental monitoring, healthcare, and smart cities. However, the limited battery life of sensor nodes is still the major challenge to energy efficiency. In this paper, we present a new method based on the Spotted hyena optimization (SHO) for optimizing the clustering in wireless sensor networks (WSNs) to maximize the network lifetime and enhance energy efficiency. The SHO movement simulates the social behaviors and cooperative prey ambush strategies of spotted hyenas, thus effectively balancing exploration and exploitation in the clustering mechanism. The proposed SHO-based clustering method selects the optimal CHs to minimize energy consumption, reduce the energy level, and maximize the transmission performance of data transmission. Using available current clustering techniques such as TEEN, LEACH, LEACH-SF, and DEEC, a performance comparison is made with the help of MATLAB 2020. The experiments show that SHO-CH-WSN can extend the network life cycle and achieve a more balanced energy distribution among different nodes. Moreover, it achieves a 4% lower total network deployment cost compared to the conventional techniques. The mortality rate of each node is improved by 4% and energy efficiency has been enhanced by 3% using the proposed network. However, the SHO has a few cautions such as falling into local optima, becoming computation-heavy with increasing network size, in addition to needing to fine-tune various parameters. In future work, we will address these limitations to improve the applicability of our approach in the wild. The results show that in the case of SHO, First nodes die after 1300 rounds and Half of the nodes die after 1425 rounds, which is more than the other metaheuristic techniques. Similarly, for energy consumption in scenario 3, 0.50 J energy is spent after 1350 rounds whereas the same amount of energy is spent in other algorithms in very less rounds. The results show how SHO is optimizing energy consumption and lifetime. The results in this work indicate that SHO is a potential solution for the design of energy-efficient WSNs and could be a candidate for the upcoming wireless communications techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. |
| Author Keywords | Network lifetime; Optimization; Spotted hyena optimization; WSN |
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