CDC: Compressive Data Collection for Wireless Sensor Networks

Data collection is a crucial operation in wireless sensor networks. The design of data collection schemes is challenging due to the limited energy supply and the hot spot problem. Leveraging empirical observations that sensory data possess strong spatiotemporal compressibility, this paper proposes a novel compressive data collection scheme for wireless sensor networks. We adopt a power-law decaying data model verified by real data sets and then propose a random projection-based estimation algorithm for this data model. Our scheme requires fewer compressed measurements, thus greatly reduces the energy consumption. It allows simple routing strategy without much computation and control overheads, which leads to strong robustness in practical applications. Analytically, we prove that it achieves the optimal estimation error bound. Evaluations on real data sets (from the GreenOrbs, IntelLab and NBDC-CTD projects) show that compared with existing approaches, this new scheme prolongs the network lifetime by 1.5X to 2X for estimation error 5-20 percent.

[1]  Linghe Kong,et al.  Optimizing the Spatio-temporal Distribution of Cyber-Physical Systems for Environment Abstraction , 2010, 2010 IEEE 30th International Conference on Distributed Computing Systems.

[2]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[3]  Yunhao Liu,et al.  Does Wireless Sensor Network Scale? A Measurement Study on GreenOrbs , 2011, IEEE Transactions on Parallel and Distributed Systems.

[4]  Deborah Estrin,et al.  A wireless sensor network For structural monitoring , 2004, SenSys '04.

[5]  David E. Culler,et al.  Experiences with a high-fidelity wireless building energy auditing network , 2009, SenSys '09.

[6]  Jörg Widmer,et al.  In-network aggregation techniques for wireless sensor networks: a survey , 2007, IEEE Wireless Communications.

[7]  Kenneth Ward Church,et al.  Very sparse random projections , 2006, KDD '06.

[8]  Xinbing Wang,et al.  Energy and latency analysis for in-network computation with compressive sensing in wireless sensor networks , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  Enrique Mallada,et al.  Compressive sensing over graphs , 2010, 2011 Proceedings IEEE INFOCOM.

[10]  R. Nowak,et al.  Compressed Sensing for Networked Data , 2008, IEEE Signal Processing Magazine.

[11]  Richard G Baraniuk,et al.  More Is Less: Signal Processing and the Data Deluge , 2011, Science.

[12]  Martin Vetterli,et al.  Network correlated data gathering with explicit communication: NP-completeness and algorithms , 2006, IEEE/ACM Transactions on Networking.

[13]  Jun Sun,et al.  Compressive data gathering for large-scale wireless sensor networks , 2009, MobiCom '09.

[14]  Richard G. Baraniuk,et al.  Bayesian Compressive Sensing Via Belief Propagation , 2008, IEEE Transactions on Signal Processing.

[15]  Xue Liu,et al.  Data loss and reconstruction in sensor networks , 2013, 2013 Proceedings IEEE INFOCOM.

[16]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[17]  R. DeVore,et al.  Compressed sensing and best k-term approximation , 2008 .

[18]  Linghe Kong,et al.  Multi-attribute compressive data gathering , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[19]  John A. Stankovic,et al.  LUSTER: wireless sensor network for environmental research , 2007, SenSys '07.

[20]  Kannan Ramchandran,et al.  A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[21]  Sachin Katti,et al.  Trading structure for randomness in wireless opportunistic routing , 2007, SIGCOMM '07.

[22]  Wei Wang,et al.  Distributed Sparse Random Projections for Refinable Approximation , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[23]  Michele Zorzi,et al.  On the interplay between routing and signal representation for Compressive Sensing in wireless sensor networks , 2009, 2009 Information Theory and Applications Workshop.

[24]  Sukun Kim,et al.  Health Monitoring of Civil Infrastructures Using Wireless Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[25]  Xiaoying Gan,et al.  Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach , 2015, IEEE Transactions on Parallel and Distributed Systems.

[26]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[27]  Yonina C. Eldar,et al.  Introduction to Compressed Sensing , 2022 .

[28]  Athanasios V. Vasilakos,et al.  Compressed data aggregation for energy efficient wireless sensor networks , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[29]  Milica Stojanovic,et al.  Random Access Compressed Sensing for Energy-Efficient Underwater Sensor Networks , 2011, IEEE Journal on Selected Areas in Communications.

[30]  Sanjay Jha,et al.  Wireless Sensor Networks for Battlefield Surveillance , 2006 .

[31]  John Anderson,et al.  An analysis of a large scale habitat monitoring application , 2004, SenSys '04.

[32]  Chris Sherlaw-Johnson,et al.  Estimating a Markov Transition Matrix from Observational Data , 1995 .

[33]  Linghe Kong,et al.  Mobility increases the surface coverage of distributed sensor networks , 2013, Comput. Networks.

[34]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[35]  Robert Tappan Morris,et al.  Opportunistic routing in multi-hop wireless networks , 2004, Comput. Commun. Rev..

[36]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[37]  Yi Qin,et al.  Converge-cast with MIMO , 2011, 2011 Proceedings IEEE INFOCOM.

[38]  Tsuhan Chen,et al.  Design, implementation and evaluation of an efficient opportunistic retransmission protocol , 2009, MobiCom '09.

[39]  Raheem A. Beyah,et al.  SMITE: A stochastic compressive data collection protocol for Mobile Wireless Sensor Networks , 2011, 2011 Proceedings IEEE INFOCOM.

[40]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.

[41]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[42]  Linghe Kong,et al.  Surface Coverage in Sensor Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[43]  Meixia Tao,et al.  Resource-Efficient Data Gathering in Sensor Networks for Environment Reconstruction , 2015, Comput. J..

[44]  S. Johansen,et al.  MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY , 2009 .