Sequence Statistical Code Based Data Compression Algorithm for Wireless Sensor Network
Tóm tắt
Sensors play an integral part in the technologically advanced real world. Wireless sensors are which have powered by batteries with limited capacity. Hence energy efficiency is one of the major issues with wireless sensors. Many techniques have been proposed in order to improve sensor efficiency. This paper discusses to improve energy efficiency of sensor through data compression. Sequence statistical code based data compression algorithm is being proposed to improve the energy efficiency of sensors. SDC and FOST codes were used in this algorithm in order to achieve better compression ratio. The simulation result was compared with arithmetic data compression techniques. In the proposed algorithm computation process is very simple than arithmetic data compression techniques.
Tài liệu tham khảo
Sheltami, T., Musaddiq, M., & Shakshuki, E. (2016). Data compression techniques in wireless sensor networks. Future Generation Computer Systems, 64, 151–162.
Li, N., Zhang, N., Das, S. K., & Thuraisingham, B. (2009). Privacy preservation in wireless sensor networks: a state-of-the-art survey. Ad Hoc Networks, 7, 1501–1514.
Li, J., & Mohapatra, P. (2007). Analytical modeling and mitigation techniques for the energy hole problem in sensor networks. Pervasive and Mobile Computing, 3, 233–254.
Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7, 537–568.
Lskshmanan, M. K., & Nikookar, H. (2006). A review of wavelets for digital wireless communication. Wireless Personal Communications, 37, 387–420.
Chang, J.-Y., & Pei-Hao, J. (2012). An efficient cluster-based power saving scheme for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2012, 172.
Xiao, J.-J., Ribeiro, A., Luo, Z.-Q., & Giannakis, G. B. (2006). Distributed compression-estimation using wireless sensor networks. IEEE Signal Processing Magazine, 23(4), 41.
Alippi, C., Anastasi, G., Di Francesco, M., & Roveri, M. (2010). An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy hungry sensors. IEEE Transcations on Instrumentation and Measurement, 59(2), 335–344.
Srisooksai, T., Keamarungsi, K., Lamsrichan, P., & Araki, K. (2012). Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35, 37–59.
Ravindra Babu, T., Narasimha Murty, M., & Agrawal, V. K. (2007). Classification of run-length encoded binary data. Pattern Recognition, 40, 321–323.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52, 2292–2330.
Abdulla, A. E. A. A., Nishiyama, H., & Kato, N. (2012). Extending the lifetime of wireless sensor networks: A hybrid routing algorithm. Computer Communications, 35, 1056–1063.
Kolo, J. G., Ang, L.-M., Shanmugam, S. A., Lim, D. W. G., & Seng, K. P. (2013). A simple data compression algorithm for wireless sensor networks. AISC, 188, 327–336.
Witten, I. H., Neal, R. M., & Cleary, J. G. (1987). Arithmetic coding for data compression. Communication of the ACM, 30(6), 520–540.
Giancarlo, R., Scaturro, D., & Utro, F. (2012). Textual data compression in computational biology: Algorithmic techniques. Computer Science Review, 6, 1–25.
Ziv, J., & Lempel, A. (1977). A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, 23(3), 337–343.
Kolo, J. G., Seng, K. P., Ang, L.-M., & Prabaharan, S. R. S. (2011). Data compression algorithm for visual information. In ICIEIS 2011, Part III, CCIS (Vol. 253, pp. 484–497). Berlin: Springer.
Jancy, S., & Jayakumar, C. (2015). Packet level data compression techniques for wireless sensor networks. Journal of Theoretical and Applied Information Technology, 75. ISSN:1992-8645.