Decision Fusion: Sparse Network vs. Dense Network

Wireless sensor networks have found many applications because of their facility for remote information monitoring and processing. One of the main applications of sensor networks is detection of an event occurrence. In these applications, a fusion center decides about an event occurrence based on the data of sensors. Here, the goal is to detect event occurrence correctly while avoiding its false detections. To reach this, either a sparse network of expensive precise sensors or a dense network of cheap sensors may be used. This paper attempts to examine these cases. It is shown that using a relatively dense network of cheap sensors may satisfy design parameters. In addition, it is shown that how a suitable decision fusion rule can reduce design costs.