TY - GEN
T1 - Optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization
AU - Priyadarshi, Rahul
AU - Chinnapurapu, Naga Raghuram
AU - Rawat, Piyush
AU - Yang, Tiansheng
AU - Rathore, Rajkumar Singh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Bacterial Foraging Optimization
KW - Coverage
KW - Energy Efficiency
KW - Node Deployment
KW - Wireless Sensor Networks
UR - https://www.scopus.com/pages/publications/105003859711
U2 - 10.1007/978-3-031-85923-6_14
DO - 10.1007/978-3-031-85923-6_14
M3 - Conference contribution
AN - SCOPUS:105003859711
SN - 9783031859229
T3 - Communications in Computer and Information Science
SP - 178
EP - 187
BT - Internet Computing and IoT and Embedded Systems, Cyber-physical Systems, and Applications - 25th International Conference, ICOMP 2024, and 22nd International Conference, ESCS 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Revised Selected Papers
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
A2 - Shenavarmasouleh, Farzan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Internet Computing and IoT, ICOMP 2024, and 22nd International Conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024
Y2 - 22 July 2024 through 25 July 2024
ER -