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Game theoretic power allocation in sparsely distributed clusters of wireless sensors (GPAS)

Sparsely distributed clusters of wireless sensors with applications ranging from state-wide wireless bridge monitoring to animal herds tracking have gained recent attention due to their special characteristics and challenging resource allocation problem. A novel optimal power allocation paradigm based on game theory is proposed to minimize the inter and intra-cluster interference. Cost functions are assigned to the transmission power, estimate of the aggregate interference generated by the cluster on the overall network, and the signal to interference and noise ratio (SINR) at the node level. It is observed that the proposed algorithm demonstrates acceptable convergence properties when used after a channel allocation scheme and is more power efficient if used in parallel with a channel allocation method. Simulation results are presented to support the proposed analysis.