Target detection is one of the most important topics in wireless sensor networks. Many studies in the literature have addressed the problem of evaluating the performance of a sensor network based on detection probability. However, it is difficult to guarantee detection probability in a sensor network since it depends on the topology of the sensor deployment and the location of the target. A sensor network without a careful sensor location arrangement may experience very low detection probability. This paper integrates collaborative fusion and sequential detection to guarantee the quality of the decisions made by a sensor network and analytically derives the average detection latency based on value fusion and decision fusion. Specifically, sensors periodically report their local measurements or decisions to a fusion center. The fusion center makes final decisions only when both the pre-defined false alarm probability and missing probability are satisfied. Otherwise, it will continue to collect data and repeat the decision making operations. Simple and elegant detection rules are provided for the collaborative sequential detection operations. Extensive simulations are conducted to show the performance of a sensor network in terms of detection latency. The correctness of the analytical results for detection latency is also verified by simulations.

A permutation σ ∈ $\frak{S}_n$ is simsun if for all k, the subword of σ restricted to {1, . . . , k} does not have three consecutive decreasing elements. The permutation σ is double simsun if both σ and σ−1 are simsun. In this paper, we present a new