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This paper investigates the application of Bacterial Foraging Optimization (BFO) for optimizing node placement in Wireless Sensor Networks (WSNs). Efficient node placement is crucial for enhancing network coverage, connectivity, and energy efficiency. BFO, inspired by bacterial foraging behavior, is particularly suited for this task due to its ability to adaptively explore and exploit search spaces. The study compares BFO against traditional optimization methods like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), highlighting its superior performance in achieving optimal node configurations. Experimental results demonstrate significant improvements in coverage, connectivity, and energy consumption metrics, validating BFO as an effective tool for optimizing WSN deployments. This research contributes insights into leveraging BFO for enhancing WSN performance and identifies avenues for further exploration in dynamic and large-scale deployment scenarios.

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Optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Rahul Priyadarshi,
  • Naga Raghuram Chinnapurapu,
  • Piyush Rawat,
  • Tiansheng Yang,
  • Rajkumar Singh Rathore

摘要

This paper investigates the application of Bacterial Foraging Optimization (BFO) for optimizing node placement in Wireless Sensor Networks (WSNs). Efficient node placement is crucial for enhancing network coverage, connectivity, and energy efficiency. BFO, inspired by bacterial foraging behavior, is particularly suited for this task due to its ability to adaptively explore and exploit search spaces. The study compares BFO against traditional optimization methods like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), highlighting its superior performance in achieving optimal node configurations. Experimental results demonstrate significant improvements in coverage, connectivity, and energy consumption metrics, validating BFO as an effective tool for optimizing WSN deployments. This research contributes insights into leveraging BFO for enhancing WSN performance and identifies avenues for further exploration in dynamic and large-scale deployment scenarios.