Huffman coding packet balancer based data compression techniques in Wireless Sensor Network

  • Authors

    • Varun Rao
    • Sandeep Nukala
    • Abirami G
    • Deepa R
    • Revathi Venkataraman
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12152
  • Sparse Recovery, Compressed Packet, Huffman Coding Packet Balancer.
  • In Wireless Sensor Networks, sensor devices perform sensing and communicating task over a network for data delivery from source to destination. Due to the heavy loaded information, during packet transmission, sensor node will drain off its energy frequently, thus led to packet loss. The novelty of the proposed work is mainly reducing the loss of packet and energy consumption during transmission. Thus, Huffman coding packet balancer select the best path between the intermediate nodes and are compared based on transmitting power, receiving and sensing power these measure the QOS in wireless sensor network.  To satisfy the QOS of the node, compressed packet from source to destination is done by choosing the best intermediate node path. The advantages of the proposed work is minimum packet loss and minimize the end to end delay. Sparse recovery is used to reconstruct the path selection when there is high density of node.

     

     
  • References

    1. [1] Maya M. Warriera*, Ajay Kumarb, ‘’An energy efficient approach for routing in wireless sensor Networks’’ Kalady, Ernakulam, 683574, Kerala, India, 2016.

      [2] Kazuhiko Kinoshita, Member, IEEE, NatsukiInoue, Nonmember Yosuke Tanigawa, Member, IEEE,HidekiTode, Member, IEEE, and Takashi Watanabe, Fellow, IEEE, ‘’Fair Routing for Overlapped Cooperative Heterogeneous Wireless Sensor Networks’’; IEEE SENSORS JOURNAL, VOL. 14, NO. 1, JANUARY 2016.

      [3] Sunil Kumar Singh, Prabhat Kumar and Jyoti Prakash Singh, ‘’An Energy Efficient Odd-Even Round Number Based Data Collection using Mules in WSNs’’; IEEE WiSPNET 2016 conference.

      [4] Dariush Ebrahimi and Chadi Assi, Senior Member, IEEE, ‘’On the Interaction Between Scheduling and Compressive Data Gathering in Wireless Sensor Networks’’; IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 15, NO. 4, APRIL 2016.

      [5] Qiang Wang Member IEEE, CuicuiLv, Yi Shen Member IEEE, Jinming Chen, ‘’Compressed Sensing and Mobile Agent Based Sparse Data Collection in Wireless Sensor Networks’’ IEEE 2015.

      [6] W. U.Bajwa, J. D. Haupt, A. M. Sayeed, and R. D. Nowak, “Joint source-channel communication for distributed estimation in sensor networks,â€IEEE Transactions on Information Theory, vol.53, no.10, pp.3629-3653, 2007

      [7] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,†Computer networks, vol.52, no.12, pp.2292-2330,2008.

      [8] M. Chen, S. Gonzalez, and V. C. Leung,“Applications and design issues for mobile agents in wireless sensor networks,†IEEE Wireless Communications,vol.14, no.6, pp.20-26,2007.

      [9] R.Baraniuk,“Compressive sensing,â€IEEE signal processing magazine, vol.24, no.4. pp.118-120, 2007.

      [10] D.L. Donoho,“Compressed sensing,â€IEEETransactions on Information Theory,vol.52, no.4,pp.1289-1306, 2006.

      [11] E. J. Cands, M. B. Wakin,“An introduction to compressive sampling,â€IEEE Signal Processing Magazine, vol.25, no.2, pp.21-30,2008.

      [12] W. U.Bajwa, J. D. Haupt, A. M. Sayeed, and R. D. Nowak, “Joint source-channel communication for distributed estimation in sensor networks,â€IEEE Transactions on Information Theory, vol.53, no.10, pp.3629-3653, 2007.

      [13] S. Ji, R. Beyah, and Z. Cai, “Snapshot and continuous data collection in probabilistic wireless sensor networks,†IEEE Trans. Mobile Comput., vol. 13, no. 3, pp. 626–637, Mar. 2014.

      [14] Srbinovski, B., Magno, M., O'Flynn, B., Pakrashi, V., &Popovici, E.Energy aware adaptive sampling algorithm for energy harvesting wireless sensor networks. Sensors Applications Symposium (SAS), IEEE 2015; 1-6.

      [15] Amarlingam M, Pradeep Kumar Mishra, K. V. V. Durga Prasad, P Rajalakshmi,"Compressed Sensing for Different Sensors: A Real Scenario for WSN and IoT " in Proc. Indian Institute of Technology Hyderabad, 2016.

      [16] T. Padmapriya, V.Saminadan, “Performance Improvement in long term Evolution-advanced network using multiple imput multiple output techniqueâ€, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, Sp-6, pp: 990-1010, 2017.

      M. Rajesh, Manikanthan, “GET-UP-AND-GO EFFICIENT MEMETIC ALGORITHM BASED AMALGAM ROUTING PROTOCOLâ€, International Journal of Pure and Applied Mathematics, ISSN NO:1314-3395, Vol-116, No. 21, Oct 2017.
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  • How to Cite

    Rao, V., Nukala, S., G, A., R, D., & Venkataraman, R. (2018). Huffman coding packet balancer based data compression techniques in Wireless Sensor Network. International Journal of Engineering & Technology, 7(2.24), 531-535. https://doi.org/10.14419/ijet.v7i2.24.12152