Inter-cluster Multi-hop Routing in Wireless Sensor Networks Employing Compressive Sensing

Compressive Sensing (CS) represents a new paradigm that addresses the problem of power consumption for collecting data over wireless sensor networks (WSN). Intercluster multi-hop routing, referred to as ICCS, is proposed as an extension to clustering in WSN utilizing CS to further reduce power consumption. With ICCS, CS measurements are relayed from each cluster head (CH) to the base station rather than being transmitted directly. A greedy algorithm is proposed to form a routing tree between the CHs and the base station. Total power consumption for networks supporting intra-cluster and inter-cluster transmission is formulated and compared to cluster based compressive sensing. Network characteristics are analyzed and optimal cases for least power consumption with ICCS are identified.

[1]  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.

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

[4]  Minh Tuan Nguyen,et al.  Compressive sensing based energy-efficient random routing in wireless sensor networks , 2014, 2014 International Conference on Advanced Technologies for Communications (ATC 2014).

[5]  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).

[6]  Minh Tuan Nguyen,et al.  Compressive Sensing Based Data Gathering in Clustered Wireless Sensor Networks , 2014, 2014 IEEE International Conference on Distributed Computing in Sensor Systems.

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

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

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

[10]  Minh Tuan Nguyen,et al.  Neighborhood based data collection in Wireless Sensor Networks employing Compressive Sensing , 2014, 2014 International Conference on Advanced Technologies for Communications (ATC 2014).

[11]  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.

[12]  Antonio Ortega,et al.  Joint Optimization of Transport Cost and Reconstruction for Spatially-Localized Compressed Sensing in Multi-Hop Sensor Networks , 2010 .

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

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

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

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

[18]  M. Punithavalli,et al.  A Survey on Clustering Algorithms , 2010 .

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

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

[21]  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.

[22]  Qi Cheng,et al.  Efficient Data Routing for Fusion in Wireless Sensor Networks , 2012 .

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

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