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Configuring Cloud-integrated Body Sensor Networks with Evolutionary Algorithms

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 verify 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.