Evolutionary game-based data aggregation model for wireless sensor networks

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes. Since multi-source data obtained from different nodes represent redundancy or complement property, as an effective tool to deal with the conflicts, the use of game theory for WSNs is provided. The authors propose a common aggregation model, which is independent of the specific application environments, based on the evolutionary game theory called evolutionary game-based data aggregation model (EGDAM) in WSNs. EGDAM made up of formal definition, functional model and general process is defined to map the competition and cooperation in aggregation procedure into games, and well-avoid perfect rationality. The authors then put the theoretic model into application. Guided by our model, an evolutionary game-based adaptive weighting algorithm named EGWDA is provided for the pixel-level data aggregation with homogeneous sensors. Reasonable weights distribution of sensors can be achieved during the aggregation in WSNs. The experiments on both the self-constructed data and the one from reference made satisfied performances.