Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework

Since communication quality between sensors can directly affect distributed estimation, we consider the communication channel pricing and sensor scheduling problem for distributed estimation over a wireless sensor network with limited resources. Each sensor's choice of channels depends on its estimation performance and the channel communication cost which sets by a communication network server. Thus, there exists a tradeoff between the estimation accuracy and the channel communication cost. To solve this decision-making process, a Stackelberg game framework is builded, where the server firstly sets pricing strategy, then sensors schedule communication channels under limited resources. In this scenario, the existence of the optimal stationary decision-making process of sensors is provided after observing the server's stationary and deterministic pricing policy. Firstly, we analyze the impact of channel pricing on the convergence of the system. Then, the server's optimal pricing strategy is proposed after observing the sensors' channel scheduling policy under a Stackelberg game framework. The property of the equilibrium pair in the Stackelberg game framework is investigated and finally an optimal channel pricing and scheduling schemes based on the equilibrium pair is proposed. Finally, simulation results verify the optimality of the channel pricing and scheduling mechanisms.