Constrained bandwidth allocation in multi-sensor information fusion: a mechanism design approach

Sensor networks are increasingly seen as a solution for a large number of environmental, security and military monitoring tasks. Typically, in these networks, noisy data from a number of local sensors is fused to reduce the uncertainty in the global picture. A central issue in this information fusion is the decision of what data should be shared between sensors, in order to maximize the global gain in information, when the bandwidth of the communication network is limited. In this paper, we study the problem from a selfish agent perspective. We show how the uncertainty in the measurement of an event can be cast as a utility function derived from the Kalman filter. We then use the tools of mechanism design to engineer an incentive-compatible mechanism that allows rational selfish agents to individually maximize their own utility, whilst ensuring that the overall utility of the system is also maximized. We apply the mechanism to multi-sensor target detection and consider the complexity of finding an efficient solution with broadcast communication protocols.