| Summary: | 碩士 === 國立臺灣科技大學 === 資訊工程系 === 102 === Recently, wireless sensor networks (WSNs) are extensively utilized in security surveillance, environmental monitoring and target tracking/detection. Although a stationary sensor network is often adequate to meet application requirements, it is not suited to many situations. For example, a huge number of nodes are required to monitor a large region. In such situations, mobile sensor networks can be used to resolve the communication and sensing coverage problems. This paper addresses the problem of detecting a target using mobile sensor networks. In target detection, one important issue is to perform detection efficiently and reduce energy consumption in transmission. Most of past studies adopted a disc model to target detection and use only 1‐bit to represent the detection made by the system. Although using the trivial detection strategy is easy to implement, the detection performance may be decreased due to high false alarm probability caused by noise. In this paper, we propose a collaborative sequential detection with multiple sensors. The detection strategy is applied to the environment where noise and signal attenuation coexists. One of the fundamental issues in target detection problems is exposure, which measures how the region is covered by the sensor network. While traditional studies focus on stationary sensor networks, this thesis formally defines and evaluates exposure in mobile sensor networks with the presence of obstacles and noises. We proposed a method for this problem named “Uniformly Most Powerful Detector(UMP)”. To conform with practical situations, detection is conducted without presuming the target’s activities and moving directions. As there is no fixed layout of node positions, a time expansion technique is developed to evaluate exposure. Since determining exposure can be computationally expensive, algorithms to calculate the upper and lower bounds on exposure are developed.
In simulation, we compare our results to a strategy which based on value fusion. Simulation results show that our method gets better performance than value fusion strategy and indicate that the strategy can determine the upper and lower bounds on exposure for any sensor route plan and sensing schedule with and without the presence of obstacles. We also use the simulation result to verify our analytical solution.
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