Advertisement

Energy Efficient Deflate (EEDeflate) Compression for Energy Conservation in Wireless Sensor Network

  • Pramod Ganjewar
  • S. Barani
  • Sanjeev J. Wagh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 530)

Abstract

WSN comprises of sensor nodes distributed spatially to accumulate and transmit measurements from environment through radio communication. It utilizes energy for all its functionality (sensing, processing, and transmission) but energy utilization in case of transmission is more. Data compression can work effectively for reducing the amount of data to be transmitted to the sink in WSN. The proposed compression algorithm i.e. Energy Efficient Deflate (EEDeflate) along with fuzzy logic works effectively to prolong the life of Wireless Sensor Network. EEDeflate algorithm saves 7% to 10% of energy in comparison with the data transmitted without compression. It also achieves better compression ratio of average 22% more than Huffman and 8% more than Deflate compression algorithm. This improvements in terms of compression efficiency allows saving energy and therefore extends the life of the sensor network.

Keywords

Wireless Sensor Networks Radio Communication Energy Consumption Data Transmission Data Compression Fuzzy Logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jennifer Yick, Biswanath Mukheerjee, and Dipak Ghosal,”Wireless Sensor Network Survey”, Elsevier, Computer Networks,2008, pp. 2292-2330.Google Scholar
  2. 2.
    M.A. Razzaque, Chris Bleakley, Simon Dobson, “Compression in Wireless Sensor Networks: A Survey and Comparative Evaluation”, ACM Transactions on Sensor Networks, Vol. 10, No. 1, Article 5, Publication date: November 2013.Google Scholar
  3. 3.
    Tossaporn Srisooksai, Kamol Keamarungsi, Poonlap Lamsrichan, Kiyomichi Araki,”Practical data compression in wireless sensor networks: A survey”,Elsevier,Journal of Network and Computer Applications,35,37–59,2012.Google Scholar
  4. 4.
    Jonathan Gana Kolo, S.Anandan Shanmugan, David Wee Gin Lim, Li-Minn Ang, “Fast and Efficient Lossless Adaptive Compression Scheme for Wireless Sensor Networks”, Elsevier, Computer and Electrical Engineering, June 2014.Google Scholar
  5. 5.
    Tommy Szalapski · Sanjay Madria,” On compressing data in wireless sensor networks for energy efficiency and real time delivery”, Springer, Distrib Parallel Databases 31,151–182, 2013.Google Scholar
  6. 6.
    Mohamed Abdelaal and Oliver Theel,” An Efficient and Adaptive Data Compression Technique for Energy Conservation in Wireless Sensor Networks”, IEEE Conference on Wireless Sensors, December 2013.Google Scholar
  7. 7.
    Emad M. Abdelmoghith, and Hussein T. Mouftah,” A Data Mining Approach to Energy Efficiency in Wireless Sensor Networks”, IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks,2013.Google Scholar
  8. 8.
    Xi Deng, and Yuanyuan Yang,” Online Adaptive Compression in Delay Sensitive Wireless Sensor Networks”, IEEE Transaction on Computers, Vol. 61, No. 10, October 2012.Google Scholar
  9. 9.
    Massimo Vecchio, Raffaele Giaffreda, and Francesco Marcelloni, “Adaptive Lossless Entropy Compressors for Tiny IoT Devices”, IEEE Transcations on Wireless Communications, Vol. 13, No. 2, Februrary 2014.Google Scholar
  10. 10.
    Mohammad Tahghighi, Mahsa Mousavi, and Pejman Khadivi, “Hardware Implementation of Novel Adaptive Version of De flate Compression Algorithm”, Proceedings of ICEE 2010, May 11-13, 2010.Google Scholar
  11. 11.
    Danny Harnik, Ety Khaitzin, Dmitry Sotnikov, and Shai Taharlev, “Fast Implementation of Deflate”, IEEE Data Compression Conference, IEEE Computer Society, 2014.Google Scholar
  12. 12.
    Wu Weimin, Guo Huijiang, Hu Yi, Fan Jingbao, and Wang Huan, “Improvable Deflate Algorithm”, 978-1-4244-1718-6/08/$25.00 ©2008 IEEE.Google Scholar
  13. 13.
    Imad S. AlShawi, Lianshan Yan, and Wei Pan, “Lifetime Enhancement in Wireless Sensor Networks Using Fuzzy Approach and A-Star Algorithm”, IEEE Sensor Journal, Vol. 12, No. 10, October 2012.Google Scholar
  14. 14.
    Ranganathan Vidhyapriya1 and Ponnusamy Vanathi,” Energy Efficient Data Compression in Wireless Sensor Networks”, International Arab Journal of Information Technology, Vol. 6, No. 3, July 2009.Google Scholar
  15. 15.
    Tommy Szalapski, Sanjay Madria, and Mark Linderman, “TinyPack XML: Real Time XML compression for Wireless Sensor Networks”, IEEE Wireless communications and Networking Conference: Service,Applications and Business, 2012.Google Scholar
  16. 16.
    S. Renugadevi and P.S. Nithya Darsini, “Huffman and Lempel-Ziv based Data Compression Algorithms for Wireless Sensor Networks”, Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering(PRIME),Februrary 21-22,2013.Google Scholar
  17. 17.
    Jaafar Kh. Alsalaet, Saleh I. Najem and Abduladhem A. Ali(SMIEEE), “Vibration Data Compression in Wireless sensor Network”,2012.Google Scholar
  18. 18.
    Mo yuanbin, Qui yubing, Liu jizhong, Ling Yanxia,”A Data Compression Algorithm based on Adaptive Huffman code for Wireless Sensor Networks”,2011 Fourth International Conference on Intelligence Computation Technology and Automation, 2011.Google Scholar
  19. 19.
    Ganjewar Pramod, Sanjeev J. Wagh and S. Barani. “Threshold based data reduction technique (TBDRT) for minimization of energy consumption in WSN”. 2015 International Conference on Energy Systems and Applications. IEEE, 2015.Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Sathyabama UniversityChennaiIndia
  2. 2.MIT Academy of Engineering, Alandi(D.)PuneIndia
  3. 3.Department of E & CSathyabama UniversityChennaiIndia
  4. 4.Department of Information TechnologyGovernment College of EngineeringKaradIndia

Personalised recommendations