In-network Processing for Collaborative Sequential Detection in Wireless Sensor Networks

• Main Authors: Yu-shan Li, 李育珊
• Other Authors: Tai-lin Chin
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/e2ej76

碩士 === 國立臺灣科技大學 === 資訊工程系 === 100 === Recently, wireless sensor networks (WSNs) are extensively utilized in security surveillance, environmental monitoring and target tracking/detection. WSNs are used to monitor the physical environment and perform tasks for a variety of purposes. In target detection, two important issues are 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. Three collaborative sequential detection methods are presented. These methods are based on value fusion by summation, value fusion by likelihood ratio product, and decision fusion by likelihood ratio product. In particular, data fusion can integrate multiple detection data, and provide an accurate and useful decision result to ensure the overall performance. By using the technology of in-network processing, sensor measurements are processed along the route to the sink and successfully reduces energy consumption of network. Simulation result shows that the integration of value fusion and in-network processing with likelihood ratio product achieves better performance. Furthermore, using in-network processing, our research reduces the energy consumption in transmission among sensors.