CCS: Energy-efficient data collection in clustered wireless sensor networks utilizing block-wise compressive sensing

Abstract In this paper, we propose an integration of compressive sensing (CS) and clustering in WSNs utilizing block diagonal matrices (BDMs) as the measurement matrices. Such an integration results in a significant reduction in the power consumption related to the data collection. The main idea is to partition a WSN into clusters, where each cluster head (CH) collects the sensor readings within its cluster only once and then generates CS measurements to be forwarded to the base station (BS). We considered two methods to forward CS measurements from CHs to the BS: (i) direct and (ii) multi-hop routing through intermediate CHs. For the latter case, a distributed tree-based algorithm is utilized to relay CS measurements to the BS. The BS then implements a CS recovery process in the collected M CS measurements to reconstruct all N sensory data, where M ≪ N . Under this novel framework, we formulated the total power consumption and discussed the effect of different sparsifying bases on the CS performance as well as the optimal number of clusters for reaching the minimum power consumption.

[1]  Ossama Younis,et al.  Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach , 2004, IEEE INFOCOM 2004.

[2]  Michael B. Wakin,et al.  The Restricted Isometry Property for block diagonal matrices , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[3]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

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

[5]  Michael B. Wakin,et al.  Concentration of measure for block diagonal measurement matrices , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Catherine Rosenberg,et al.  Compressed Data Aggregation: Energy-Efficient and High-Fidelity Data Collection , 2013, IEEE/ACM Transactions on Networking.

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

[8]  S.A.G. Chandler,et al.  Calculation of number of relay hops required in randomly located radio network , 1989 .

[9]  Mohamed F. Younis,et al.  Fault-tolerant clustering of wireless sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[10]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[11]  Ravi Prakash,et al.  Load-balancing clusters in wireless ad hoc networks , 2000, Proceedings 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology.

[12]  Wu-chi Feng,et al.  RIDA: A Robust Information-Driven Data Compression Architecture for Irregular Wireless Sensor Networks , 2007, EWSN.

[13]  Jun Sun,et al.  Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering , 2010, IEEE Transactions on Wireless Communications.

[14]  Edward J. Coyle,et al.  An energy efficient hierarchical clustering algorithm for wireless sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[15]  Richard G. Baraniuk,et al.  The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range , 2011, IEEE Transactions on Signal Processing.

[16]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[17]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

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

[19]  Catherine Rosenberg,et al.  Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks? , 2010, 2010 IEEE International Conference on Communications.

[20]  Piotr Indyk,et al.  Sparse Recovery Using Sparse Random Matrices , 2010, LATIN.

[21]  Mina Sartipi,et al.  Energy-Efficient Data Acquisition in Wireless Sensor Networks Using Compressed Sensing , 2011, 2011 Data Compression Conference.

[22]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

[23]  Robert D. Nowak,et al.  Decentralized compression and predistribution via randomized gossiping , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[24]  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.

[25]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, NBiS.

[26]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[27]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[28]  Minh Tuan Nguyen,et al.  Minimizing energy consumption in random walk routing for Wireless Sensor Networks utilizing Compressed Sensing , 2013, 2013 8th International Conference on System of Systems Engineering.

[29]  Dirk Timmermann,et al.  Low energy adaptive clustering hierarchy with deterministic cluster-head selection , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[30]  Ahmad Habibizad Navin,et al.  HEECH: Hybrid Energy Effective Clustering Hierarchical Protocol for Lifetime Prolonging in Wireless Sensor Networks , 2010, 2010 International Conference on Computational Intelligence and Communication Networks.

[31]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[32]  Michele Zorzi,et al.  Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework , 2012, IEEE Transactions on Wireless Communications.

[33]  Xiaohua Jia,et al.  Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[34]  Xiaohua Jia,et al.  Minimum Transmission Data Gathering Trees for Compressive Sensing in Wireless Sensor Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[35]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[36]  Minh Tuan Nguyen,et al.  Tree-based energy-efficient data gathering in wireless sensor networks deploying compressive sensing , 2014, 2014 23rd Wireless and Optical Communication Conference (WOCC).

[37]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[38]  Theodore S. Rappaport,et al.  Wireless Communications: Principles and Practice (2nd Edition) by , 2012 .

[39]  Nazanin Rahnavard,et al.  Cluster-Based Energy-Efficient Data Collection in Wireless Sensor Networks Utilizing Compressive Sensing , 2013, MILCOM 2013 - 2013 IEEE Military Communications Conference.

[40]  Shaojie Tang,et al.  Data gathering in wireless sensor networks through intelligent compressive sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[41]  Jie Wu,et al.  An energy-efficient unequal clustering mechanism for wireless sensor networks , 2005, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005..

[42]  Nazanin Rahnavard,et al.  Inter-cluster Multi-hop Routing in Wireless Sensor Networks Employing Compressive Sensing , 2014, 2014 IEEE Military Communications Conference.

[43]  Trac D. Tran,et al.  Fast and Efficient Compressive Sensing Using Structurally Random Matrices , 2011, IEEE Transactions on Signal Processing.