Mobile Agent-Based Directed Diffusion in Wireless Sensor Networks
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Research Article Mobile Agent-Based Directed Diffusion in Wireless Sensor Networks Min Chen,1 Taekyoung Kwon,2 Yong Yuan,3 Yanghee Choi,2 and Victor C. M. Leung1 1 Department
of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4 of Computer Science and Engineering, Seoul National University, Seoul 151-744, South Korea 3 Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China 2 School
Received 29 November 2005; Revised 12 May 2006; Accepted 16 July 2006 Recommended by Deepa Kundur In the environments where the source nodes are close to one another and generate a lot of sensory data traffic with redundancy, transmitting all sensory data by individual nodes not only wastes the scarce wireless bandwidth, but also consumes a lot of battery energy. Instead of each source node sending sensory data to its sink for aggregation (the so-called client/server computing), Qi et al. in 2003 proposed a mobile agent (MA)-based distributed sensor network (MADSN) for collaborative signal and information processing, which considerably reduces the sensory data traffic and query latency as well. However, MADSN is based on the assumption that the operation of mobile agent is only carried out within one hop in a clustering-based architecture. This paper considers MA in multihop environments and adopts directed diffusion (DD) to dispatch MA. The gradient in DD gives a hint to efficiently forward the MA among target sensors. The mobile agent paradigm in combination with the DD framework is dubbed mobile agent-based directed diffusion (MADD). With appropriate parameters set, extensive simulation shows that MADD exhibits better performance than original DD (in the client/server paradigm) in terms of packet delivery ratio, energy consumption, and end-to-end delivery latency. Copyright © 2007 Min Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Recent years have witnessed a growing interest in deploying a sheer number of microsensors that collaborate in a distributed manner on sensing, data gathering, and processing. In contrast with IP-based communication networks based on global addresses and routing metrics of hop counts, sensor nodes normally lack global addresses. Also, as being unattended after deployment, they are constrained in energy supply (e.g., small battery capacity). These characteristics of sensor networks require energy awareness at most layers of protocol stacks. To address such challenges, most of researches focus on prolonging the network lifetime, allowing scalability for a large number of sensor nodes, or supporting fault tolerance (e.g., sensor’s failure and battery depletion) [2, 3]. Most energy-efficient proposals are based on the traditional client/server computing model, where each sensor node sends its sensory data to a
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