Research Article
Configuring Cloud-integrated Body Sensor Networks with Evolutionary Algorithms
@INPROCEEDINGS{10.4108/icst.bodynets.2014.257098, author={Yi Cheng-Ren and Junichi Suzuki and Dung Phan and Shigo Omura and Ryuichi Hosoya}, title={Configuring Cloud-integrated Body Sensor Networks with Evolutionary Algorithms}, proceedings={9th International Conference on Body Area Networks}, publisher={ICST}, proceedings_a={BODYNETS}, year={2014}, month={11}, keywords={body area networks body sensor networks cloud-integrated body sensor networks evolutionary game theory}, doi={10.4108/icst.bodynets.2014.257098} }- Yi Cheng-Ren
Junichi Suzuki
Dung Phan
Shigo Omura
Ryuichi Hosoya
Year: 2014
Configuring Cloud-integrated Body Sensor Networks with Evolutionary Algorithms
BODYNETS
ACM
DOI: 10.4108/icst.bodynets.2014.257098
Abstract
This paper investigates a few evolutionary game theoretic algorithms to configure cloud-integrated body sensor networks (BSNs) in an adaptive and stable manner with a multi-tier architecture called Body-in-the-Cloud (BitC). BitC allows BSNs to adapt their configurations (sensing intervals and sampling dates as well as data transmission intervals) to operational conditions (e.g., data request patterns) with respect to multiple conflicting performance objectives such as resource consumption and data yield. BitC theoretically guarantees that each BSN performs an evolutionarily stable configuration strategy, which is an equilibrium solution under given operational conditions. Simulation results ver- ify this theoretical analysis; BSNs seek equilibria to perform adaptive and evolutionarily stable configuration strategies. BitC outperforms an existing well-known genetic algorithm in the quality, stability and computational cost in configuring BSNs.