Abstract
In recent years, wireless sensor networks (WSNs) have gained significant attention due to their wide range of applications in monitoring and surveillance. A WSN consists of numerous sensor nodes that can communicate with each other and sense specific targets within their area of deployment. This paper introduces a multi-phase protocol that combines Differential Evolution (DE) and Simulated Annealing (SA) to optimize target coverage in wireless sensor networks (WSNs). The proposed protocol is designed to systematically reduce sensor redundancy while ensuring comprehensive coverage. Through a sequence of well-defined phases, our method achieves significant improvements in coverage efficiency while also activating fewer sensors compared to traditional approaches. Index Terms—Wireless Sensor Networks (WSNs), Target Coverage, Differential Evolution (DE), Simulated Annealing (SA), Optimization, Sensor Deployment, k-Coverage.
J. Chikosa and H. M. Ammari—Contributed equally to this work.
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Acknowledgment
We would like to thank the National Science Foundation and the Department of Engineering at Texas A&M University-Kingsville. Both organizations provided invaluable assistance and resources.
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Davenport, J., Chikosa, J., Ammari, H.M. (2025). Optimizing Target Coverage in Wireless Sensor Networks: A Hybrid Differential Evolution and Simulated Annealing-Based Approach. In: Zhang, S., Zhang, LJ. (eds) Internet of Things – ICIOT 2024. SCF 2024 - ICIOT 2024. Lecture Notes in Computer Science, vol 15427. Springer, Cham. https://doi.org/10.1007/978-3-031-77003-6_7
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