Contention-Based Transmission for Decentralized Detection

In this work, we propose a contention-based protocol for general decentralized detection problem in the context of wireless sensor networks. In this scheme, fusion task is implemented in a multi-stage fashion: sensors are first grouped according to the informativeness of their data; fusion center then polls the sensor sets sequentially in the order of their informativeness until a target performance is reached. Within one stage, all polled sensors compete for a common channel medium where exists near-far effect, Raleigh fading, and shadowing. To determine the optimal transmission probability, we propose a novel Bayesian update algorithm utilizing both sensing information and channel feedback. The proposed dynamic protocol is applied to signal detection in Gaussian noise. As shown by our simulations, incorporating sensing information greatly improves efficiency over a generic Bayesian update scheme relying only on channel feedback. Our results also show that exploiting capture effect can significantly improve communication and energy efficiency. Comparison with fixed sample size test and sequential probability ratio test shows that the proposed scheme achieves significant efficiency gain over existing fusion strategies.