Energy efficient data diffusion in wireless sensor networks

  • Authors:
  • R. B. Patel;Deepika Jain

  • Affiliations:
  • M.M. Engineering College, Mullana, Ambala, Haryana, India;M.M. Engineering College, Mullana, Ambala, Haryana, India

  • Venue:
  • Proceedings of the International Conference on Advances in Computing, Communication and Control
  • Year:
  • 2009

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Abstract

The wireless sensor network is an emerging technology that may greatly facilitate human life by providing ubiquitous sensing, computing, and communication capability, through which people can more closely interact with the environment whenever required. One of the most important problems studied in any sensor network is data fusion. Client/server paradigm has been a commonly used computing model in traditional distributed wireless sensor networks (DWSNs). However, the deployment of wireless sensor networks (WSNs) and its ad hoc nature have brought new challenges to the fusion task. For example, the advances in sensor technology allow better, cheaper, and smaller sensors to be used, which results in a much larger number of sensors deployed. On the other hand, sensors communicate through wireless networks where the network bandwidth is much lower than for wired communication. In this article, we have presented mobile agent (MA) assisted data fusion in WSNs (MADFWs). In MADFWs, data stay at the local site, while the fusion process (code) is moved to the data sites. By transmitting the computing process instead of data, network bandwidth requirement is largely reduced and the performance of the fusion process is more stable. One of the key issue which is implemented in this model is how to plan the itinerary for a MA in order to achieve progressive fusion accuracy. We have presented a method to develop an optimal itinerary for MA to fulfill the integration task while consuming minimum amount of resources, including time and power. In MADFWs agent has freedom to invite some nearby slave sensors to cooperatively position the object and inhibit other irrelevant (i.e., farther) sensors from tracking the object. As a result, the communication and sensing overheads are greatly reduced.