Optimal Performance for Detection Systems in Wireless Passive Sensor Networks

Passive wireless sensors have emerged as a new technology to measure a vast majority of phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security, among many others. The deployment of wireless passive sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. In this paper, we formulate the detection performance, with non-ideal channels, in a probabilistic way, and compare with classical detection performance. We design an optimal adaptive Neyman-Pearson detector, given the channel probabilistic model, by formulating and solving a constrained optimization problem.