移动协助传感器网络中Sink的路径优化策略

doi:10.3969/j.issn.1000-436x.2013.02.010

Abstract

Abstract:

There inevitably exist some serious problems such as energy hole,overlapping and hot spots in static wireless sensor networks which are composed by all static sensors.The mobile Sink (MS) was used to reduce the energy consumption of static sensor nodes through a collection-based approach in which a subset of nodes served as the data collection points (CP) that buffer data originated from sensors and transferred these data to MS when it arrived.An optimization model named min-energy min-distance (MEMD) of MS’ moving path was introduced and proved this model was NP-hard.A heuristic algorithm was developed combining MS and CPs selection to enable a flexible trade-off between energy consumption and data delivery latency.Furthermore,a probabilistic path selection (PPS) algorithm to make the MS visit as much as possible sensors was proposed.The experimental and simulating results show monotonic decrease of data delivery latency for greater limits on the energy consumption and vice versa.

Key words: wireless sensor network, mobile Sink, path selection, visit probability, optimization

Xi-wei ZHANG,Lin SHEN,Yi-feng JIANG. Optimizing path selection of mobile Sink nodes in mobility-assistant WSN[J]. Journal on Communications, 2013, 34(2): 85-93.

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References 16

[1]	TOLLE G , POLASTRE J , SZEWCZYK R ,et al. Amacroscope in the redwoods[A]. ACM SenSys[C]. San Diego,USA, 2005. 51-63.
[2]	MAYER K , ELLIS K , TAYLOR K . Cattle health mon-itoring using wireless sensor networks[A]. Proc of the ACM IASTED[C]. Cambridge,Massachusetts,USA, 2004. 375-380.
[3]	XUE W , LUO Q , CHEN L ,et al. Contour mapmatching for event detection in sensor networks[A]. Proc of the ACM SIGMOD[C]. Chicago,USA, 2006. 375-380.
[4]	DU W , FANG L , NING P . Lad:localization anomaly detection for wireless sensor networks[A]. Proc of the IEEE IPDPS[C]. Denver,Colorado, 2005. 121-127.
[5]	ASLAM J , BUTLER Z , CONSTANTIN F ,et al. Tracking a moving object with a binary sensor network[A]. Proc of the ACM SenSys[C]. San Diego,USA, 2005. 150-161.
[6]	SRINIVASAN W W V , CHUA K C . Trade-offs between mobility and density for coverage in wireless sensor networks[A]. Proc of the ACM MobiCom[C]. Montreal,Quebec,Canada, 2007. 39-50.
[7]	LI Z J , LI M , WANG J L ,et al. Ubiquitous data collection for mobile users in wireless sensor networks[A]. Proc of the IEEE INFOCOM[C]. Shanghai,China, 2011. 2246-2254.
[8]	LUO J , ZHANG Q , WANG D . Delay tolerant event collection for underground coal mine using mobile sinks[A]. Proc of the IEEE IWQoS[C]. Charleston,USA, 2009. 1-9.
[9]	SUGIHARA R , GUPTA R K . Optimizing energy-latency trade-off in sensor networks with controlled mobility[A]. Proc of the IEEE INFOCOM[C]. Rio,Brazil, 2009. 1398-1408.
[10]	XING G L , WANG T , JIA W J ,et al. Rendezvous design algorithms for wireless sensor networks with a mobile base station[A]. Proc of the ACM MobiHoc[C]. Hongkong,China, 2008. 231-240.
[11]	郜帅, 张宏科 . 时延受限传感器网络移动 Sink 路径选择方法研究[J]. 电子学报, 2011,39(4): 742-747. GAO S , ZHANG H K . Optimal path selection for mobile Sink in delayguaranteed sensor networks[J]. Acta Electronica Sinica, 2011,39(4): 742-747.
[12]	程龙, 陈灿峰, 马建 . 无线传感器网络中多移动 Sink 的选择策略[J]. 通信学报, 2008,29(11): 12-18. CHENG L , CHEN C F , MA J . Selection schema of mobile Sinks in wireless sensor networks[J]. Journal on Communications, 2008,29(11): 12-18.
[13]	KIM H S , ABDELZAHER T F , KWON W H . Dynamic delay-constrained minimum-energy dissemination in wireless sensor networks[J]. ACM Transactions on Embedded Computing System, 2005,4(3): 679-706.
[14]	CROSSBOW . Mica and mica2 wireless measurement system datasheets[EB/OL]. 2004.
[15]	ZHAO J , GOVINDAN R . Understanding packet delivery performance in dense wireless sensor networks[A]. Proc of the ACM SenSys[C]. Los Angeles,USA, 2003. 311-320.
[16]	张希伟, 陈贵海 . 基于SDMA应用的移动Sink节点的设计与实现[J]. 计算机研究与发展, 2012,49(3): 541-549. ZHANG X W , CHEN G H . Design of mobile Sink node for SDMA applications in WSN[J]. Journal of Computer Research and Development, 2012,49(3): 541-549.

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