Hierarchical Distributed Compressed Sensing for Wireless Sensor Network

CHENG Yinbo; SI Jingjing; HOU Xiaolan

doi:10.11999/JEIT160439

Volume 39 Issue 3

Mar. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(3): 539-545

CHENG Yinbo, SI Jingjing, HOU Xiaolan. Hierarchical Distributed Compressed Sensing for Wireless Sensor Network[J]. Journal of Electronics & Information Technology, 2017, 39(3): 539-545. doi: 10.11999/JEIT160439

Citation:

CHENG Yinbo, SI Jingjing, HOU Xiaolan. Hierarchical Distributed Compressed Sensing for Wireless Sensor Network[J]. Journal of Electronics & Information Technology, 2017, 39(3): 539-545. doi: 10.11999/JEIT160439

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Hierarchical Distributed Compressed Sensing for Wireless Sensor Network

doi: 10.11999/JEIT160439

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)

Received Date: 2016-05-03
Rev Recd Date: 2016-11-23
Publish Date: 2017-03-19

Abstract

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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References(18)

References

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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