期刊文献+

数据传输时延和跳数受限的Sink节点移动路径选择算法 被引量:17

Sink Node Moving Path Selection Algorithm Limited by Data Transmission Delay and Hops
下载PDF
导出
摘要 考虑实际无线传感网系统中数据传输时延和跳数受限情况,且为降低算法的时间复杂度,提出一种移动无线传感网的Sink节点移动路径选择算法(MPSA)。在MPSA算法中,Sink节点采用分布式最短路径树算法收集k+1跳通信范围内传感节点的相关信息和感知数据,采用虚拟力理论计算边界、障碍物和空洞区域的虚拟斥力、第k+1跳未覆盖传感节点的虚拟引力和所有虚拟力的合力,根据停留次数、合力大小和方向等信息计算当前网格中心的停留时间和下一个停留网格中心。仿真结果表明:MPSA算法根据传感节点的位置、剩余能量等信息,寻找到一条较优的移动路径,从而提高Sink节点的数据收集量和节点覆盖率,降低传感节点的感知数据丢弃量。总之,在数据传输时延和跳数受限下,MPSA算法比RAND算法、GMRE算法和EASR算法更优。 Considering that data transmission delay and hops are limited in actual system,and to reduce the timecomplexity of algorithm,sink node moving path selection algorithm(MPSA)in mobile wireless sensor networks isproposed. In MPSA algorithm,sink node uses distributed shortest path tree algorithm to gather relevant informationand data of sensor nodes in k+1-hop communication range. It uses virtual force theory to calculate the virtual repul-sive forces of boundaries,obstacles and void regions,virtual gravitational forces of non-covered k+1-hop sensornodes and resultant force of all virtual forces. It calculates residence time at present grid center and next residencegrid center based on the information such as number of residence,size and direction of the resultant force. Simula-tion results show that according to the information such as node position and residual energy,MPSA algorithm canfind an appropriate moving path of sink node,improve the gathering data amount and node coverage rate of sinknode,and reduce the drop amount of sensor nodes' sensed data. In short,when data transmission delay and hopsare limited,MPSA algorithm outperforms RAND algorithm,GMRE algorithm and EASR algorithm.
出处 《传感技术学报》 CAS CSCD 北大核心 2016年第4期583-592,共10页 Chinese Journal of Sensors and Actuators
基金 浙江省自然科学基金项目(LY14F030006 LY15F030004) 国家自然科学基金项目(61501403) 浙江省公益性技术应用研究计划项目(2015C33028) 浙江省教育厅项目(Y201432498)
关键词 移动无线传感网 路径选择 虚拟力 数据传输时延 数据传输跳数 mobile wireless sensor networks path selection virtual force data transmission delay data transmission hop
  • 相关文献

参考文献14

  • 1Zhu C, Shu L, Hara T, et al. A Survey on Communication and Da- ta Management Issues in Mobile Sensor Networks [J]. Wireless Communications and Mobile Computing, 2014, 14( 1 ) : 19-36.
  • 2Khan A W, Abdullah A H, Anisi M H, et al. A Comprehensive Study of Data Collection Schemes Using Mobile Sinks in Wireless Sensor Networks[J]. Sensors, 2014,14(2) : 2510-2548.
  • 3Rao J, Biswas S. Data Harvesting in Sensor Networks Using Mobile Sinks [ J ]. IEEE Wireless Communications, 2008,15 (6) : 63 -70.
  • 4Keskin M E, Altinel I K, Aras N, et al. Lifetime Maximization in Wireless Sensor Networks Using a Mobile Sink with Nonzero Trav- eling Time[J]. The Computer Journal,2011,54(12) : 1987-1999.
  • 5郭剑,孙力娟,许文君,王汝传,肖甫.基于移动sink的无线传感器网络数据采集方案[J].通信学报,2012,33(9):176-184. 被引量:17
  • 6Liu W, Lu K, Wang Ji, et al. Performance Analysis of Wireless Sensor Networks with Mobile Sinks [J]. IEEE Transactions on Vehicular Technology, 2012,61 (6) : 2777-2789.
  • 7Kumar A K, Sivalingam K M, Kumar A, et al. On Reducing Delay in Mobile Data Collection Based Wireless Sensor Networks [J]. Wireless Network, 2013,19 ( 3 ) : 285-299.
  • 8王章权,陈友荣,尉理哲,任条娟.优化网络生存时间的Sink节点移动路径选择算法[J].传感技术学报,2014,27(3):409-415. 被引量:8
  • 9Salarian H, Chin K W, Naghdy F, et al. An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks [J]. IEEE Transactions on Vehicular Technology, 2014, 63 (5) : 2407-2419.
  • 10Lee K, Kim Y H, Kim H J, et al. A Myopic Mobile Sink Migration Strategy for Maximizing Lifetime of Wireless Sensor Networks [J]. Wireless Networks, 2014,20(2) : 303-318.

二级参考文献59

  • 1陶丹,马华东,刘亮.基于虚拟势场的有向传感器网络覆盖增强算法[J].软件学报,2007,18(5):1152-1163. 被引量:93
  • 2Gage D W. Command control for many-robot systems. In: Proceedings of the 19th Annual AUVS Technical Sympo- sium. Huntsville, USA: Unmanned Systems, 1992. 28-34.
  • 3Wang G L, Cao G H, Porta T F L. Movement-assisted sen- sor deployment. IEEE Transactions on Mobile Computing, 2006, 5(6): 640-652.
  • 4Cortes J, Martinez S, Karatas T, Bullo F. Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 2004, 20(2): 243-255.
  • 5Heo N, Varshney P K. Energy-efficient deployment of intel- ligent mobile sensor networks. IEEE Transactions on Sys- tems, Man, and Cybernetics, Part A: Systems and Humans, 2005, 35(1): 78-92.
  • 6Jorgic M, Stojmenovic I, Hauspie M, Simplot-Ryl D. Lo- calized algorithms for detection of critical nodes and links for connectivity in ad hoc networks. In: Proceedings of the 3rd Annual Mediterranean Ad Hoc Networking Workshop. Bodrum, Turkey: IEEE, 2004. 360-371.
  • 7Das S, Liu H, Nayak A, Stojmenovic I. A localized algorithm for bi-connectivity of connected mobile robots. Telecommu- nication Systems, 2008, 40(3-4): 129-140.
  • 8Zou Y, Chakrabarty K. Sensor deployment and target local- ization based on virtual forces. In: Proceedings of the 22nd Annual Joint Conference of the IEEE Computer Communi- cations. Washington D. C., USA: IEEE, 2003. 1293-1303.
  • 9Howard A, Mataric M J, Sukhatme G S. Mobile sensor net- work deployment using potential field: a distributed scalable solution to the area coverage problem. In: Proceedings of the 6th International Symposium on Distributed Autonomous Robotic Systems. Fukuoka, Japan: Springer, 2002. 299-308.
  • 10Tan G, Jarvis S A, Kermarrec A M. Connectivity- guaranteed and obstacle-adaptive deployment schemes for mobile sensor networks. In: Proceedings of the 28th In- ternational Conference on Distributed Computing Systems. Washington D. C., USA: IEEE, 2008. 429-437.

共引文献64

同被引文献113

引证文献17

二级引证文献61

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部