A measurement allocation scheme for reliable data gathering in spatially correlated sensor networks

Abstract

In this paper, we consider the measurement allocation problem in a spatially correlated sensor field. Our goal is to determine the probability of each sensor's being measured based on its contribution to the estimation reliability; it is desirable that a sensor improving the estimation reliability is measured more frequently. We consider a correlation model reflecting transmission power limit, noise in measurement process and channel, and channel attenuation. Then the estimation reliability is defined as the distortion error between the event source in the sensor field and its estimation at the sink. Motivated by the correlation nature, we model the measurement allocation problem into a cooperative game, and then express each sensor's contribution using Shapley value — a formal quantification of individual player's average marginal contribution. Against the intractability in the computation of exact Shapley value, we deploy randomized method that enables to compute approximate Shapley value within reasonable time. In numerical experiments, we evaluate approximate Shapley value by comparing it to the exact one, and illustrate that measurement allocation according to Shapley value turns to the balance between the estimation reliability and network lifetime.