A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA


Energy is one of the most important items to determine the network lifetime due to low power energy nodes included in the network. Generally, data aggregation tree concept is used to find an energy efficient solution. However, even the best aggregation tree does not share the load of data packets to the transmitting nodes fairly while it is consuming the lowest possible energy of the network. Therefore, after some rounds, this problem causes to consume the whole energy of some heavily loaded nodes and hence results in with the death of the network. In this paper, by using the Genetic Algorithm (GA), we investigate the energy efficient data collecting spanning trees to find a suitable route which balances the data load throughout the network and thus balances the residual energy in the network in addition to consuming totally low power of the network. Using an algorithm which is able to balance the residual energy among the nodes can help the network to withstand more and consequently extend its own lifetime. In this work, we calculate all possible routes represented by the aggregation trees through the genetic algorithm. GA finds the optimum tree which is able to balance the data load and the energy in the network. Simulation results show that this balancing operation practically increases the network lifetime.

Share and Cite:

A. Norouzi, F. Babamir and Z. Orman, "A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA," Wireless Sensor Network, Vol. 4 No. 8, 2012, pp. 191-196. doi: 10.4236/wsn.2012.48028.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] K. Kalpakis, K. Dasgupta and P. Namjoshi, “Maximum Lifetime Data Gathering and Aggregation in Wireless Sensor Networks,” IEEE International Conference on Networking, Singapore, 27-30 August 2002, pp. 685-696.
[2] K. Dasgupta, K. Kalpakis and P. Namjoshi, “An Efficient Clustering-Based Heuristic for Data Gathering and Aggregation in Sensor Networks,” IEEE Wireless Communications and Networking Conference, New Orleans, 20 March 2003, pp. 1948-1953.
[3] S. Hussain and O. Islam, “An Energy Efficient Spanning Tree Based Multi-Hop Routing in Wireless Sensor Network,” Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), Hong Kong, 11-15 March 2007, pp. 4383-4388. doi:10.1109/WCNC.2007.799
[4] H. O. Tan and I. K?rpeo?lu, “Power Efficient Data Gathering and Aggregation in Wireless Sensor Networks,” SIGMOD Record, Vol. 32, No. 4, 2003, pp. 66-71. doi:10.1145/959060.959072
[5] O. Islam and S. Hussain, “Genetic Algorithm for Data Aggregation Trees in Wireless Sensor Networks,” 3rd International Conference on Intelligent Environments, Ulm, 24-25 September 2007, pp. 312-316. doi:10.1049/cp:20070386
[6] S. Jin, M. Zhou and A. S. Wu, “Sensor Network Optimization Using a Genetic Algorithm,” Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, 30 March-2 April 2003, pp. 109-116.
[7] K. P. Ferentinos, T. A. Tsiligiridis and K. G. Arvanitis, “Energy Optimization of Wirless Sensor Networks for Environmental Measurements,” Proceedings of the International Conference on Computational Intelligence for Measurment Systems and Applicatons (CIMSA), Giardini Naxos, 20-22 July 2005, pp. 250-255. doi:10.1109/CIMSA.2005.1522872
[8] F. G. Nakamura, “Planning to Control Dynamic Coverage and Connectivity in Wireless Sensor Networks Planas,” Master’s Thesis, Federal University of Minas Gerais, Belo Horizonte, 2003.
[9] D. Goldberg, B. Karp, Y. Ke, S. Nath and S. Seshan, “Genetic Algorithm in Search, Optimization, and Machine Learning,” Addison-Wesley, Boston, 1989.
[10] F. P. Quint?o, F. G. Nakamura and G. R, Mateus, “A Hybrid Approach to Solve the Coverage and Connectivity Problem in Wireless Sensor Networks,” 4th European Workshop on Meta-Heuristics: Design and Evaluation of Advanced Hybrid Meta-Heuristics, Nottingham, 3-4 No- vember 2004, pp. 1-5.
[11] A. P. Bhondekar, R. Vig, M. L. Singla, C. Ghanshyam and P. Kapur, “Genetic Algorithm Based Node Placement Methodology for Wireless Sensor Network,” Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong, 18-20 March 2009, pp. 106-112.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.