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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jennifer Yick, Biswanath Mukheerjee, and Dipak Ghosal,”Wireless Sensor Network Survey”, Elsevier, Computer Networks,2008, pp. 2292-2330.
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.
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.
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.
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.
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.
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.
Xi Deng, and Yuanyuan Yang,” Online Adaptive Compression in Delay Sensitive Wireless Sensor Networks”, IEEE Transaction on Computers, Vol. 61, No. 10, October 2012.
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.
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.
Danny Harnik, Ety Khaitzin, Dmitry Sotnikov, and Shai Taharlev, “Fast Implementation of Deflate”, IEEE Data Compression Conference, IEEE Computer Society, 2014.
Wu Weimin, Guo Huijiang, Hu Yi, Fan Jingbao, and Wang Huan, “Improvable Deflate Algorithm”, 978-1-4244-1718-6/08/$25.00 ©2008 IEEE.
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.
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.
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.
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.
Jaafar Kh. Alsalaet, Saleh I. Najem and Abduladhem A. Ali(SMIEEE), “Vibration Data Compression in Wireless sensor Network”,2012.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Ganjewar, P., Barani, S., Wagh, S.J. (2016). Energy Efficient Deflate (EEDeflate) Compression for Energy Conservation in Wireless Sensor Network. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-47952-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47951-4
Online ISBN: 978-3-319-47952-1
eBook Packages: EngineeringEngineering (R0)