A Dynamic Pricing Scheme for Congestion Game in Wireless Machine-to-Machine Networks

Abstract

The problem of assigning a set of source nodes to a set of routes in wireless machine-to-machine (M2M) networks is addressed using a game theoretic approach. The objective is to minimize the maximum latency over all source nodes as far as possible while the game achieves a pure Nash Equilibrium (NE). To compute such an NE efficiently, we present a distributed dynamic pricing (DP) scheme, where each source node is assumed to pay for using any route so that the route has incentive to relay data for the source node. A loose upper bound is given for the convergence time of DP, and simulation results show that it performs much faster in practice. The price of anarchy in this game is also investigated by comparing DP with a cost-reducing path method; the results show that DP produces optimum assignment in more than 90% of the simulation runs.