Large-scale multiple-source detection using wireless sensor networks

This dissertation concerns the sequential large-scale detection of multiple potential sources using wireless sensor networks. A new 2-step approach to sequential multiple-source detection is introduced called the iterative partial sequential probability ratio test (IPSPRT) that minimizes the time-to-decision as the desired probability of false alarm and probability of miss decrease. The first step of the IPSPRT sequentially decides whether any or no sources become active at a specific time, based on a sequential probability ratio test using the generalized likelihood ratio such that the probability of indecision is minimized and the maximum probability of false alarm and maximum probability of miss are bounded. If step one decides that some source is active, step two identifies active sources through an iterative maximization of the likelihood ratio and physical inspection process such that the probability that an active source is not detected is bounded. After a decision is made regarding sources which become active at a specific time, the IPSPRT increments the time at which sources are hypothesized to become active and the procedure continues. Numerical evaluations of the IPSPRT are provided in comparison to other feasible methods for a diffusion process monitoring example consisting of 100 sensors and 100 potential sources. A new dynamic sensor selection problem is formulated for the non-Bayesian multiple source detection problem using a generalized likelihood ratio based dynamic sensor selection strategy (GLRDSS) which a minimum number of sensors to report observations at each sampling instance. An evaluation of the GLRDSS is provided through simulation. A carbon sequestration site monitoring application is introduced as a case study and a test bed implementation discussed. The robustness of the IPSPRT and dynamic sensor selection algorithm to common wireless sensor networking errors and failures is evaluated using the carbon sequestration site monitoring application as a case study.