Scalable continuous object detection and tracking in sensor networks

Authors:
Shin-Chih Tu;Guey-Yun Chang;Jang-Ping Sheu;Wei Li;Kun-Ying Hsieh
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan, ROC;Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan, ROC;Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, ROC;Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan, ROC;Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan, ROC
Venue:
Journal of Parallel and Distributed Computing
Year:
2010

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Abstract

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.