Random Access for Decentralized Detection in Wireless Sensor Networks

In this work, we propose a random access protocol for decentralized detection in wireless sensor networks. In this scheme, sensors are grouped according to the informativeness of their data. Then fusion center collects sensor data sequentially in the order of their informativeness and terminates the fusion process once the target performance is reached. To determine the optimal transmission probability in random access, we propose a novel Bayesian update algorithm utilizing both the sensing information and the channel feedback. As shown by our simulations, incorporating sensing information greatly improves the communication efficiency over a generic Bayesian update scheme relying only on channel feedback. Comparison with fixed sample size test and sequential probability ratio test shows that the proposed scheme achieves significant channel and power efficiency gain over existing strategies.