Object Tracking in Wireless Sensor Networks by Mobile Agent and Mining Movement Patterns

• Main Authors: Chung-han Tsai, 蔡宗翰
• Other Authors: Wei-Bo Lee
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/39319774144972700996

碩士 === 國立中山大學 === 資訊管理學系研究所 === 98 === With the advances of wireless communications and micro-electronic device technologies, wireless sensor networks have been applied in a wide spectrum of applications, including one of the killer applications--object tracking. Among numerous challenges in object tracking, one of the important issues is the energy management. One solution to the above issue is the mobile agent-based paradigm. Using the mobile agent in wireless sensor networks has many advantages over the client/server paradigm in terms of energy consumptions, networks band-width, etc. In this thesis, we adopt the mobile agent-based paradigm to support object track-ing in wireless sensor networks. Although using the mobile agents for object tracking can improve the overall perfor-mance, the hurdle is the determination of the mobile agent itinerary. The past studies on ob-ject tracking considered the object’s movement behavior as randomness or the direction and the speed of the object remain constant for a certain period of time. However, in most real-world cases, the object movement behavior is often based on certain underlying events rather than randomness complete. With this assumption, the movements of objects are some-times predictable. Through the prediction, the mobile agent can determine which node to mi-grate in order to reduce energy consumption and increase the performance of object tracking. In this thesis, we develop a mining-based approach to discover the useful patterns from the object’s movement behavior. This approach utilizes the discovered rules to choose the sensor node the mobile agent needs to migrate in order to reduce the number of wrong migration, to reduce total energy consumed by sensor nodes, and to prolong the lifetime of the wireless sensor network. Experimental results show the efficiency of the proposed approach.