An Evolutionary Game Theoretic Approach for Configuring Cloud-Integrated Body Sensor Networks

Abstract

This paper considers a cloud-integrated architecture for body sensor networks (BSNs), called Body-in-the-Cloud (BitC), and studies an evolutionary game theoretic approach to configure BSNs in an adaptive and stable manner. BitC allows BSNs to adapt their configurations (i.e., Sensing intervals and sampling rates as well as data transmission intervals for nodes) to operational conditions (e.g., Data request patterns) with respect to multiple conflicting objectives such as resource consumption and data yield. Moreover, BitC allows each BSN to perform an evolutionarily stable configuration strategy, which is an equilibrium solution under given operational conditions. Simulation results show that BitC effectively configures BSNs by seeking the trade-offs among conflicting objectives.