适用于无线传感器网络的层次化分布式压缩感知

程银波; 司菁菁; 候肖兰

doi:10.11999/JEIT160439

适用于无线传感器网络的层次化分布式压缩感知

doi: 10.11999/JEIT160439

基金项目:

国家自然科学基金(61471313, 61303128)，河北省自然科学基金(F2014203183)，燕山大学青年教师自主研究计划课题(13LGB015)，秦皇岛市科学技术研究与发展计划(201602A031)

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出版历程
- 收稿日期: 2016-05-03
- 修回日期: 2016-11-23
- 刊出日期: 2017-03-19

Hierarchical Distributed Compressed Sensing for Wireless Sensor Network

Funds:

The National Natural Science Foundation of China (61471313, 61303128), The Natural Science Foundation of Hebei Province (F2014203183), The Youth Foundation of Yanshan University (13LGB015), The Science and Technology Plan of Qinhuangdao (201602A031)

摘要

摘要: 分布式压缩感知(Distributed Compressed Sensing, DCS)是在无线传感器网络(Wireless Sensor Network, WSN)中减少数据传输量、降低能量消耗的有效手段。该文面向分簇WSN，提出层次化分布式压缩感知(Hierarchical Distributed Compressed Sensing, HDCS)。在利用簇内DCS消除簇内时间、空间冗余的基础上，利用簇间DCS消除簇间空间冗余，减少簇头的数据发送量。针对分簇WSN采集信号的结构化稀疏特性，建立块稀疏簇内联合稀疏模型与块稀疏簇间联合稀疏模型，提出HDCS观测方案与层次化联合重构算法。仿真结果表明，与普通DCS相比，HDCS在保证重建信号质量的同时，能够有效减轻簇头的通信负担，并显著降低Sink上的信号重构时间。
- 无线传感器网络 /
- 分布式压缩感知 /
- 层次化分布式压缩感知 /
- 分簇
Abstract: Distributed Compressed Sensing (DCS) is an effective means to reduce the amount of data transmission and energy consumption in Wireless Sensor Network (WSN). Hierarchical Distributed Compressed Sensing (HDCS) is proposed for clustering WSN. It eliminates the temporal-spatial redundancies among data collected by the cluster members with the intra-cluster DCS, and eliminates the spatial redundancies among clusters with the inter-cluster DCS. According to the signals structured sparsity, a block-sparse intra-cluster joint sparsity model and a block-sparse inter-cluster joint sparsity model are constructed. Then, a hierarchical measurement scheme and a hierarchical joint reconstruction scheme are proposed for HDCS. Experimental results show that compared to general DCS, HDCS can relieve the transmission burden in the network effectively, without lowering the quality of the reconstructed signal. Moreover, it can reduce the signal reconstruction time at the Sink observably.
- Wireless Sensor Network (WSN) /
- Distributed Compressed Sensing (DCS) /
- Hierarchical Distributed Compressed Sensing (HDCS) /
- Cluster

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参考文献(18)

CHEN H, SHI Q, TAN R, et al. Mobile element assisted cooperative localization for wireless sensor networks with obstacles[J]. IEEE Transactions on Wireless Communications, 2010, 9(3): 956-963. doi: 10.1109/TWC. 2010.03.090706.

CHEN H, GAO F, MARTINS M, et al. Accurate and efficient node localization for mobile sensor networks[J]. ACM Mobile Networks and Applications, 2013, (18): 141-147. doi: 10.1007/s11036-012-0361-7.

PRASATH K and SHANKAR T. RMCHS: ridge method based cluster head selection for energy efficient clustering hierarchy protocol in WSN[C]. Proceedings of International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, Chennai, India, 2015: 64-70.

唐宏, 王惠珠. 基于无线信号不规则性的无线传感网层次型拓扑控制算法[J]. 电子与信息学报, 2015, 37(9): 2246-2253. doi: 10.11999/JEIT141626.

TANG Hong and WANG Huizhu. Wireless signal irregularity based hierarchical topology control algorithm for wireless sensor networks[J]. Journal of Electronics Information Technology, 2015, 37(9): 2246-2253. doi: 10.11999/ JEIT141626.

DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/ TIT.2006.871582.

BARON D, DUARTE M, SARVOTHAM S, et al. An information-theoretic approach to distributed compressed sensing[C]. Proceedings of the 43rd Allerton Conference on Communication, Control, and Computing, Monticello, USA, 2005: 814-825.

NGUYEN M and RAHNAVARD N. Cluster-based energy- efficient data collection in wireless sensor networks utilizing compressive sensing[C]. Proceedings of IEEE Military Communication Conference, San Diego, USA, 2013: 1708-1713.

XIE R and JIA X. Transmission-efficient clustering method for wireless sensor networks using compressive sensing[J]. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(3): 806-815. doi: 10.1109/TPDS.2013.90.

NGUYEN M, TEAGUE K, and RAHNAVARD N. Inter- cluster multi-hop routing in wireless sensor networks employing compressive sensing[C]. Proceedings of IEEE Military Communication Conference, Baltimore, USA, 2014: 1133-1138.

XU X, ANSARI R, KHOKHAR A, et al. Hierarchical data aggregation using compressive sensing(HDACS) in WSNs[J]. ACM Transactions on Sensor Networks, 2015, 11(3): 1-25. doi: 10.1145/2700264.

LIU D, ZHOU Q, ZHANG Z, et al. Cluster-based energy- efficient transmission using a new hybrid compressed sensing in WSN[C]. Proceedings of IEEE Conference on Computer Communications Workshops, San Francisco, USA, 2016: 372-376.

ELDAR Y, KUPPINGER P, and BOLCSKEI H. Block- sparse signals: Uncertainty relations and efficient recovery[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054. doi: 10.1109/TSP.2010.2044837.

SUNDMAN D, CHATTERJEE S, and SKOGLUND M. Distributed greedy pursuit algorithms[J]. Signal Processing, 2014, 105(12): 298-315. doi: 10.1016/j.sigpro.2014.05.027.

司菁菁, 候肖兰, 程银波. 基于块剪枝多路径匹配追踪的多信号联合重构[J]. 系统工程与电子技术, 2016, 38(9): 1993-1999. doi: 10.3969/j.issn.1001-506X.2016.09.05.

SI Jingjing, HOU Xiaolan, and CHENG Yinbo. Joint multi-signal reconstruction based on block pruning multipath matching pursuit[J]. Systems Engineering and Electronics, 2016, 38(9): 1993-1999. doi: 10.3969/j.issn.1001-506X.2016. 09.05.

CHEN H, LIU B, HUANG P, et al. Mobility-assisted node localization based on TOA measurements without time synchronization in wireless sensor networks[J]. ACM Mobile Networks and Applications, 2012, 17(1): 90-99. doi: 10.1007/s11036-010-0281-3.

CHEN H, WANG G, WANG Z, et al. Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2012, 11(1): 108-116. doi: 10.1109/TWC. 2011.110811.101739.

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