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  • 标题:Region Based Scheduling With Multiple Mobile Robots for Data Collection Strategies in Wireless Sensor Networks
  • 本地全文:下载
  • 作者:V. Karthik
  • 期刊名称:International Journal of Innovative Research in Science, Engineering and Technology
  • 印刷版ISSN:2347-6710
  • 电子版ISSN:2319-8753
  • 出版年度:2013
  • 卷号:2
  • 期号:7
  • 页码:3416
  • 出版社:S&S Publications
  • 摘要:Data collection process is one of the important aspects in the design consideration for future analysis inwireless sensor networks. Energy consumption by parent node increases due to continuous forwarding of sensed datafrom their respective child nodes especially in the tree based topology. Once the energy in the parent nodes wascompletely drained off, some of the child nodes get isolated/ partitioned from the sink node. The proposed datacollection method involves deployment of multiple mobile robots whose responsibility is to collect the data from thenodes whose energy is below the threshold value. Navigation of mobile robots to collect the data from partitionednodes usually achieved by time and location based strategies. In proposed hybrid scheduling, the navigation of mobilerobots scheduled by both the combination of time and location based approaches with multiple region scheduling. Inlarge network scenario, the mobile robot gets more burdens due to its more responsibilities to visit all partitioned nodes.So the entire scenario is divided in to different regions and the deployment of multiple mobile robots is based on therequirements. Therefore, the efficiency of sensed data collected by the base station or sink node frompartitioned/islanded WSN is improved doubly using multiple mobile robots. Through simulation under the environmentof NS-2 simulator, the results from various aspects show that proposed multiple mobile robots can improve theperformance of collecting the sensed data in large-scale sensing fields and also it improves the lifetime of the sensornodes.
  • 关键词:Multiple mobile robots; Wireless Sensor Network; Data collection process; Region Based approach; Hybrid Scheduling.
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