Probability Based Algorithms for Guaranteeing the Stability of Rechargeable Wireless Sensor Networks

详细信息    查看全文

• 作者：Yiyi Gao (24)
  Ce Yu (24)
  Jian Xiao (24)
  Jizhou Sun (24)
  Guiyuan Jiang (24)
  Hui Wang (24)
  
• 关键词：Wireless sensor network ; network lifetime ; energy routing ; probability model
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8630
• 期：1
• 页码：100-113
• 全文大小：329 KB
• 参考文献：1. Liang, W., Schweitzer, P., Xu, Z.: Approximation Algorithms for Capacitated Minimu Forest Problems in Wireless Sensor Networks with a Mobile Sink. IEEE Transactions on Computers?62(10) (2012)
  2. Buragohain, C., Agrawal, D., Suri, S.: Power Aware Routing For Sensor Database. Proceedings of INFOCOM (2005)
  3. Cristescu, R., Beferull-Lozano, B., Vetterli, M.: On Network Correlated Data Gathering. In: Proceedgins of INFOCOM (2004)
  4. Vieira, M.A.M., Coelho, C.N.: Survey on Wireless Sensor Network Devices. In: Emerging Technologies and Factory Automation, vol.?1, pp. 537-44 (2003)
  5. Bari, A., Teng, D., Jaekel, A.: Optimal Relay Node Placement in Hierachical Sensor Networks with Mobile Data Collector. In: Global Telecommunications Conference, pp. 1- (2011)
  6. Feng, S., Liang, W., Luo, J., Xu, X.: Network lifetime maximation for time-sensitive data gathering in wireless sensor network. Computer Networks 11(1), 47-0 (2012)
  7. Liu, W., Fan, J., Zhang, S., Wang, X.: Relay Hop Constrained Rendezvous Algorithm for Mobile Data Gathering in Wireless Sensor Networks. In: Hsu, C.-H., Li, X., Shi, X., Zheng, R. (eds.) NPC 2013. LNCS, vol.?8147, pp. 332-43. Springer, Heidelberg (2013) CrossRef
  8. Tan, H.O., Korpeoglu, I.: Power Efficient Data Gathering and Aggregation in Wireless Sensor Network. SIGMOD Record?32(4), 66-1 (2003) CrossRef
  9. Faith, S., Mohamed, Y.: Optimized Connectivity Restoration in a Partitioned Wireless Sensor Network. Information Processing Letters?69, 53-7 (2011)
  10. Izzet, F.S., Kemal, A., Rith, S., Mohamed, Y.: Connectivity Restoration in Disjoint Wireless Sensor Networks using Limited Number of Mobile Relays. IEEE Transactions on Mobile Computing?11(1), 47-0 (2012) CrossRef
  11. Khuller, S., Raghavachari, B., Neal, Y.: Balancing Minimum Spanning and Shortest Path Trees. IEEE Transactions on Mobile Computing?11(1), 47-0 (2012) CrossRef
  12. Wadaa, A., Olariu, S., Wilson, L., Jones, K., Xu, Q.: On Training a Sensor Networks. In: Proc. of Parallel and Distributed Processing Symp. (2003)
  13. Ataul, B., Da, T., Arunita, J.: Maximum Flow Based Model and Method of the Maximum Lifetime Problem of Sensor Network. In: Intelligent Control and Automation, pp. 21-3 (2006)
  14. Sharaf, M.A., Beacer, J., Labrinidis, A., Chrysanthis, P.A.: Balancing Energy Effcientcy and Quality of Aggregate Data in Sensor Networks. Journal of VLDB (2004)
  
• 作者单位：Yiyi Gao (24)
  Ce Yu (24)
  Jian Xiao (24)
  Jizhou Sun (24)
  Guiyuan Jiang (24)
  Hui Wang (24)
  
  24. School of Computer Science and Technology, Tianjin University, Tianjin, China
  
• ISSN：1611-3349

文摘

The lifetime of a wireless sensor network can be prolonged by introducing mobile sink to balance the energy consumption in data gathering. However, it doesn’t solve the bottleneck of energy consumption and network will still fail inevitably. The option of recharging a relay opens up new possibilities for prolonging the network lifetime even maintaining the network living forever by recharging a sensor using a mobile sink when necessary. In this paper, we investigate the strategies for the mobile sink to recharge the wireless network in order to maintain the stability of network achieving the requirement of network system. In our approach, we develop an efficient algorithm to divide the sensor network into a number of domains ?(independent communication areas which are consisted by a cluster of neighbored sensors) in the first stage. Then we iteratively calculate the maximum lifetime of each domain. When travelling and arriving at a domain, mobile sink determines whether or not recharge it according to the probability model. The lifetime of a domain is estimated based on the residual energy of each sensor within the domain using our energy routing algorithm. We also propose a probability model for the mobile sink to determine whether to recharge a domain or not. The effectiveness of our approach has been verified by extensive simulation results.