Collaborative sequential detection in surveillance sensor networks

Target detection is an important problem in wireless sensor networks where a number of sensors form a network to detect the presence or absence of a certain target or event. Data fusion is a potential method broadly used to improve detection performance when the sampling data are noisy. However, low detection probability cannot be avoided if detection decisions are made based on a collection of sampling data taken at just one particular moment. This paper adopts fusion-based sequential detection to guarantee the quality of detection results. A fusion center is used to collect local data from individual sensors periodically. A final detection decision is made only after the pre-defined constraints of false alarm and missing probability are satisfied. Rules for each sensor to make local decisions and for the fusion center to make global decisions are derived. Simulations are conducted to show the latency of making the final decisions based on the proposed fusion scheme.