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AEESPAN: Automata Based Energy Efficient Spanning Tree for Data Aggregation in Wireless Sensor Networks

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DOI: 10.4236/wsn.2009.14039    6,006 Downloads   10,433 Views   Citations

ABSTRACT

In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Z. ESKANDARI and M. YAGHMAEE, "AEESPAN: Automata Based Energy Efficient Spanning Tree for Data Aggregation in Wireless Sensor Networks," Wireless Sensor Network, Vol. 1 No. 4, 2009, pp. 316-323. doi: 10.4236/wsn.2009.14039.

References

[1] Z. Eskandari, M. H. Yaghmaee, and A. H. Mohajerzade, “Energy efficient spanning tree for data aggregation in wireless sensor networks,” ICCCN, 2008.
[2] F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey, computer networks,” Computer Networks Journal, 2002.
[3] Y. Hu, N. Yu, X. H. Jia, “Energy efficient real time data aggregation in wireless sensor network,” IWCMC, 2006.
[4] M. Lee and V. W. S. Wong, “An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks,” IEEE, 2005.
[5] J. N. Al-Karaki, and A. E. Kamal, “Routing techniques in wireless sensor networks: A survey, supported by the ICUBE initiative of Iowa State University, Ames.
[6] O. Younis and S. Fahmy, “HEED: A hybrid, energy- efficient, distributed clustering approach for ad hoc sensor networks,” IEEE, 2004.
[7] B. Krishnamachari, D. Estrin, and S. Wicker, “The impact of data aggregation in wireless sensor networks,” International Workshop on Distributed Event-Based Systems, 2002.
[8] M. Lee and V. W. S. Wong, “LPT for data aggregation in wireless sensor networks,” IEEE GLOBECOM, 2005.
[9] W. Zhang and G. Cao, “DCTC: Dynamic convoy tree- based collaboration for target tracking in sensor networks,” IEEE, 2004.
[10] S. Upadhyayula, V. Annamalai, and S. K. S. Gupta, “A low latency and energy-efficient algorithm for conver- gecast,” IEEE GLOBECOM, 2003.
[11] Z. eskandari, M. H. Yaghmaee, A. H. Mohajerzade, “Automata based energy efficient spanning tree for data aggregation in wireless sensor networks,” IEEE ICCS, 2008.
[12] S. Upadhyayula, V. Annamalai, and S. K. S. Gupta, “A lowlatency and energy-efficient algorithm for convergecast,” EEE GLOBECOM, 2003.
[13] Y. P. Chen, A. L. Liestman and J. Liu, “A hierarchical energy-efficient framework for data aggregation in wireless sensor networks,” IEEE, 2006.
[14] P. Radivojac, U. Korad, K. M. Sivalingam and Z. Obradovic, “Learning from class-imbalanced data in wireless sensor networks,” IEEE VTC, Fall 2003.
[15] P. Beyens, M. Peeters, K. Steenhaut, and A. Nowe, “Routing with compression in aireless sensor networks: A Q-learning approah,” AAMAS, 2005.
[16] M. Esnaashari, M. R. Meybodi,” “A learning automata based data aggregation method doe sensor networks,” CSICC, 2007.
[17] M. Ankit, M. Arpit, T. J. Deepak, R. Venkateswarlu and D. janakiram, “TinyLAP: A scalable learning automata- based energy aware routing protocol for sensor networks,” IEEE, 2006.
[18] Y. P. Chen, A. L. Liestman, J. Liu, “Energy-efficient data aggregation hierarchy for wireless sensor networks,” Proceedings of the 2nd Int'l Conf. on Quality of Service in Heterogeneous Wired/Wireless Networks, 2005.
[19] J. Kamimura, N. Wakamiya, and M. Murata, “Energy- efficient clustering method for data gathering in sensor networks,” BROADNETS, 2004.
[20] S. Upadhyayula and S. K. S. Gupta, “Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (DAC) in wireless sensor networks,” Elsevier, 2006.

  
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