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Effective Compressive Sensing for Clustering in Wireless Sensor Networks

Affiliations

  • Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida - 201301, Uttar Pradesh, India

Abstract


Wireless sensor Networks consists of a huge number of little powered nodes typically in the range of hundreds to thousands in number that are multifunctional and randomly deployed in a hostile environment. If a node detects an abnormal event, it will automatically send a hop by hop warning message to the sink node. There are various challenges and design issues in WSN like node deployment, routing, energy consumption, clustering, fault tolerance, coverage, connectivity and QoS i.e. Quality of Service. The author proposed a clustering approach that adopts a hybrid Compressive Sensing (CS) for sensor networks. The method compares the number of transmissions in the data aggregation techniques commonly used: Shortest path tree with hybrid compressive sensing, clustering without compressive sensing, optimal tree with hybrid compressive sensing, shortest path tree without compressive sensing, clustering with hybrid compressive sensing. Compressive sensing uses the largely similar data of large scale Wireless sensor networks and aims to minimize data transmissions without compromising the precision of the result obtained from data. The author finds that in comparison to these methods the proposed approach can reduce the data transmissions.

Keywords

Cluster Head, Compressive Sensing, Shortest Path Tree, Wireless Sensor Networks.

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References


  • Szewczyk R, Mainwaring A, Polastre J, Anderson J, Culler D. An analysis of a large scale habitat monitoring application. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems; 2004. p. 214–26.
  • Hamamoto T, Nagao S, Aizawa K. Real-time objects tracking by using smart image sensor and FPGA. Image Processing, Proceedings. International Conference. 2002; 3.
  • Cao G, Yu F, Zhang B. Improving network lifetime for wireless sensor network using compressive sensing. High Performance Computing and Communications (HPCC); 2011. p. 448–54.
  • Guan X, Guan L, Wang XG, Ohtsuki T. A new load balancing and data collection algorithm for energy saving in wireless sensor networks. Telecommunication Systems. 2010; 45(4):313–22.
  • Liu M, Cao J, Chen G, Wang X. An energy-aware routing protocol in wireless sensor networks. Sensors. 2009; 9(1):445–62.
  • Foucart S, Rauhut H. A mathematical introduction to compressive sensing, Basel: Birkhäuser. 2013; 1(3).
  • Baraniuk RG. Compressive sensing. IEEE Signal Processing Magazine. 2007; 24(4).
  • Baraniuk RG, Cevher V, Duarte MF, Hegde C. Model-based compressive sensing. IEEE Transactions on Information Theory. 2010; 56(4):1982–2001.
  • Duarte MF, Baraniuk RG. Spectral compressive sensing. Applied and Computational Harmonic Analysis. 2013; 35(1):111–29.
  • Cevher V, Sankaranarayanan A, Duarte MF, Reddy D, Baraniuk RG, Chellappa R. Compressive sensing for background subtraction. Computer Vision–ECCV, Springer Berlin Heidelberg; 2008. p. 155–68.
  • Fornasier M, Rauhut H. Compressive sensing. Handbook of Mathematical Methods in Imaging, Springer New York; 2011. p. 187–228.
  • Candes E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems. 2007; 23(3):969.
  • Kashin BS, Temlyakov VN. A remark on compressed sensing. Mathematical Notes. 2007; 82(5–6):748–55.
  • Chen G, Li C, Ye M, Wu J. An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks. 2009; 15(2):193–207.
  • Ding P, Holliday J, Celik A. Distributed energy-efficient hierarchical clustering for wireless sensor networks. Distributed Computing in Sensor Systems. 2005:322–39.
  • Chan H, Perrig A. ACE: An emergent algorithm for highly uniform cluster formation. Wireless Sensor Networks. 2004:154–71.
  • Anker T, Bickson D, Dolev D, Hod B. Efficient clustering for improving network performance in wireless sensor networks. Wireless Sensor Networks. 2008:221–36.
  • Nieberg T, Dulman S, Havinga P, van Hoesel L, Wu J. Collaborative algorithms for communication in wireless sensor networks. 2003:271–94.
  • Camilo T, Carreto C, Silva JS, Boavida F. An energy-efficient ant-based routing algorithm for wireless sensor networks. Ant Colony Optimization and Swarm Intelligence. 2006:49–59.
  • Qu MZ. Research on the applications and characteristics of the wireless sensor network. Applied Mechanics and Materials. 2014.
  • Nagdive AS, Ingole PK. An implementation of energy efficient data compression and security mechanism in clustered wireless sensor network. 2015 International Conference on Advances in Computer Engineering and Applications; 2015.

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