TY - GEN
T1 - Brain storm optimization algorithm for full area coverage of wireless sensor networks
AU - Zhu, Haoyu
AU - Shi, Yuhui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/4/7
Y1 - 2016/4/7
N2 - Coverage problem is a fundamental issue in designing efficient wireless sensor networks, in which both coverage rate and energy consumption should be considered. A brain storm optimization algorithm is a swarm intelligence algorithm which is inspired by the human brainstorming process. This paper will focus on the application of the brain storm optimization algorithm in full coverage problems of wireless sensor networks. The full coverage problems are divided into two types: problems either with fixed or flexible number of activated sensor nodes. Binary detection model and grid based strategy will be used in describing the mathematic model of the full coverage problem, which will be applied to test the effectiveness of the brain storm optimization algorithm for solving coverage problems of wireless sensor networks in different areas. Experimental results on irregular areas even with obstacles illustrate the efficiency and effectiveness of the brain storm optimization algorithm for solving full coverage problems of wireless sensor networks. In addition, if the number of activated sensor nodes is flexible, with an appropriate weight coefficient, the brain storm optimization algorithm can obtain a reasonable number of sensor nodes to realize full coverage.
AB - Coverage problem is a fundamental issue in designing efficient wireless sensor networks, in which both coverage rate and energy consumption should be considered. A brain storm optimization algorithm is a swarm intelligence algorithm which is inspired by the human brainstorming process. This paper will focus on the application of the brain storm optimization algorithm in full coverage problems of wireless sensor networks. The full coverage problems are divided into two types: problems either with fixed or flexible number of activated sensor nodes. Binary detection model and grid based strategy will be used in describing the mathematic model of the full coverage problem, which will be applied to test the effectiveness of the brain storm optimization algorithm for solving coverage problems of wireless sensor networks in different areas. Experimental results on irregular areas even with obstacles illustrate the efficiency and effectiveness of the brain storm optimization algorithm for solving full coverage problems of wireless sensor networks. In addition, if the number of activated sensor nodes is flexible, with an appropriate weight coefficient, the brain storm optimization algorithm can obtain a reasonable number of sensor nodes to realize full coverage.
KW - brain storm optimization
KW - coverage optimization
KW - wireless sensor networks
UR - https://www.scopus.com/pages/publications/84966659274
U2 - 10.1109/ICACI.2016.7449796
DO - 10.1109/ICACI.2016.7449796
M3 - Conference Proceeding
AN - SCOPUS:84966659274
T3 - Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016
SP - 14
EP - 20
BT - Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Advanced Computational Intelligence, ICACI 2016
Y2 - 14 February 2016 through 16 February 2016
ER -