The problem of communicating sensor readings over a multiaccess channel for detecting a target using Type-Based Multiple Access (TBMA) is considered. TBMA is analyzed in a general framework by considering non-i.i.d. data and non-identical channel gains. An asymptotically optimal detector is proposed and its error-exponents for detection probabilities are characterized using tools from large deviations theory. In case of i.i.d. channel gains, it is shown that the performance of TBMA presents two distinct behaviors depending on whether the channel gains have zero mean. Numerical simulations are used to demonstrate that the error exponents provide reasonably accurate estimates of the performance of TBMA.

Abstract—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. Index Terms—Neyman-Pearson detection, intermittent observations, wireless passive sensor, ROC curve. I.

Abstract — The decentralized detection performance, using wireless passive sensor networks, is analyzed according to the minimum probability of error criterion. Passive sensors communicate their measurements to the reader using data network packets, and therefore, the two main phenomena affecting the detection performance are packet loss and packet delay. In this paper, we formulate the decentralized detection problem with passive sensors and show that the optimal decision rule with packet loss is the likelihood ratio test. We present a comparative analysis study between detection with ideal and non-ideal channels, for the problem of DC level detection in White Gaussian Noise. We validate the analytical results using Monte Carlo Simulation study. Finally, we present a simple scheme for adaptive detector design, to restore the original detection performance, with the cost of increasing the delay for detection. I.