Game theoretic approach towards energy-efficient task distribution in wireless sensor networks

WSNs have a wide variety of applications, and their usability for remote monitoring of various parameters of interest is growing dramatically. Conventional applications mostly involved a single WSN for collecting raw parameters with limited aggregation on the node side, whereby more sophisticated data mining was implemented by the end-users. Recent applications however, often require more intelligent functions, in which nodes are expected to implement logical decision-makings on the aggregated data. Implementing such functions often involves more processing, and wider interactions amongst network peers, hence resulting in higher energy consumption and shorter node lifetime. Sensomax is an agent-based WSN middleware, which facilitates seamless integration of mathematical functions in large-scale wireless sensor networks. In this paper, we will investigate game theoretic and auction-based techniques in order to optimise task distribution and energy consumption in WSNs.