Abstract
In this paper, an Adaptive-Weighted Time-Dimensional and Space-Dimensional (AWTDSD) data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network. AWTDSD contains three phases: (1) the time-dimensional aggregation phase for eliminating the data redundancy; (2) the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and (3) the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station. AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well. Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.
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References
A. Gallais, J. Carle, D. Simplot-Ryl, et al.. Localized sensor area coverage with low communication overhead. IEEE Transactions on Mobile Computing, 7 (2008)5, 661–672.
H. O. Tan, I. Korpeoglu, and I. Stojmenovic. A distributed and dynamic data gathering protocol for sensor networks. 21st International Conference on Advanced Networking and Applications (AINA’07), Ontario, Canada, May 21–23, 2007, 220–227.
K. Maraiya, K. Kant, and N. Gupta. Wireless sensor network: a review on data aggregation. International Journal of Scientific & Engineering Research, 2(2011) 4, 1–6.
V. Akila and T. Sheela. Overview of data aggregation based routing protocols in wireless sensor network. International Journal of Emerging Technology and Advanced Engineering, 3(2013)1, 185–191.
Kai-Wei Fan, Sha Liu, and Prasun Sinha. Structure-free data aggregation in sensor networks. IEEE Transactions on Mobile Computing, 6(2007)8, 929–942.
H. Yousefi, M. H. Yeganeh, N. Alinaghipour, et al.. Structure-free real-time data aggregation in wireless sensor networks. Computer Communications, 35(2012) 9, 1132–1140.
Wen-Hwa Liao, Yu-Cheng Kao, and Chien-Ming Fan. Data aggregation in wireless sensor networks using ant colony algorithm. Journal of Network and Computer Applications, 31(2008)4, 387–401.
Fengyuan Ren, Jiao Zhang, Yongwei Wu, et al.. Attribute-aware data aggregation using potential-based dynamic routing in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(2013)5, 881–892.
S. J. Park and R. Sivakumar. Energy efficient correlated data aggregation for wireless sensor networks. International Journal of Distributed Sensor Networks, 4(2008)1, 13–27.
S. Lee, S. Kim, D. Ko, et al.. Prediction based mobile data aggregation in wireless sensor network. Lecture Notes in Computer Science, 5529(2009), 328–339.
H. O. Tan, I. Korpeoglu, I. Stojmenovic. Computing localized power-efficient data aggregation trees for sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(2011)3, 489–500.
Wei Xing, Kuntai Li, Yongchao Wang, et al.. A spanning routing tree protocol based on state diffusion for mobile sensor networks. 2011 International Conference on Wireless Communications and Signal Processing (WCSP’11), Nanjing, China, November 9–11, 2011, 1–5.
Z. Rehena, S. Roy, and N. Mukherjee. A modified SPIN for wireless sensor networks. 2011 Third International Conference on Communication Systems and Networks (COMSNETS’11), Bangalore, India, January 4–8, 2011, 1–4.
Jisoo Shin and Changjin Sun. CREEC: chain routing with even energy consumption. IEEE Communications and Networks, 13(2011)1, 17–25.
D. Kumar, T. C. Aseri, and R. B. Patel. EECDA: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks. International Journal of Computers, Communications & Control, 6(2011)1, 113–124.
K. Maraiya, K. Kant, and N. Gupta. Efficient cluster head selection scheme for data aggregation in wireless sensor network. International Journal of Computer Applications, 23(2011)9, 10–18.
H. Kim. An efficient clustering scheme for data aggregation considering mobility in mobile wireless sensor networks. International Journal of Control and Automation, 6(2013)1, 221–234.
B. Abid, W. Elghazel, H. Seba, et al.. An event-driven clustering scheme for data aggregation in real-time wireless sensor networks. 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA’13), Barcelona, Spain, March 25–28, 2013, 48–55.
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Supported by the Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (No. BS2010DX010) and the Project of Higher Educational Science and Technology Program of Shandong Province (No. J12LN36).
Communication author: Zhang Junhu, born in 1974, male, Associate Professor.
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Zhang, J., Zhu, X. & Peng, H. An adaptive-weighted two-dimensional data aggregation algorithm for clustered wireless sensor networks. J. Electron.(China) 30, 525–537 (2013). https://doi.org/10.1007/s11767-013-3097-z
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DOI: https://doi.org/10.1007/s11767-013-3097-z
Key words
- Data aggregation
- Adaptive-weighted aggregation
- Clustered Wireless Sensor Networks (WSNs)
- Linear regression
- Data accuracy
- Energy consumption
- Lempel-Ziv-Welch (LZW)