Tree Based Data Aggregation to Resolve Funneling Effect in Wireless Sensor Network

Authors: G. Rajesh, B. Vinayaga Sundaram, C. Aarthi

Abstract:

In wireless sensor network, sensor node transmits the sensed data to the sink node in multi-hop communication periodically. This high traffic induces congestion at the node which is present one-hop distance to the sink node. The packet transmission and reception rate of these nodes should be very high, when compared to other sensor nodes in the network. Therefore, the energy consumption of that node is very high and this effect is known as the “funneling effect”. The tree based-data aggregation technique (TBDA) is used to reduce the energy consumption of the node. The throughput of the overall performance shows a considerable decrease in the number of packet transmissions to the sink node. The proposed scheme, TBDA, avoids the funneling effect and extends the lifetime of the wireless sensor network. The average case time complexity for inserting the node in the tree is O(n log n) and for the worst case time complexity is O(n2).

Keywords: Data Aggregation, Funneling Effect, Traffic Congestion, Wireless Sensor Network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110738

References:

[1] Akyildiz I. F., Su, W., Sankarasubramaniam, Y., And Cyirci, E, "Wireless sensor networks: A survey", Computer Network. 38, 4 , 393– 422,2002.
[2] A. Alemdar, M. Ibnkahla.”Wireless Sensor Networks: Applications and Challenges”, IEEE,2007.
[3] M. Bala Krishna, Noble Vashishta.”Energy Efficient Data Aggregation Techniques in Wireless Sensor Networks”, 2013 5th International Conference on Computational Intelligence and Communication Networks.
[4] Chi Trung Ngo, Hoon Oh. “A tree-based mobility management using message aggregation based on a skewed wait time assignment in infrastructure based MANETs”-Wireless Netw (2014) 20:537–552.
[5] Chih-Hsiao Tsai, Hao-Yi Huang, Chih-Wei Hung and Ying-Hong Wang. “TDAM: A Tree-based Data Aggregation Mechanism in Wireless Sensor Networks”, 2012 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2012) November 4-7, 2012.
[6] Chunping Wang, Weihong Wang, Jian Jiao and Feihang Ge. “A Multihop and Load Balanced Routing Protocol Oriented to the Neighbors of the Sink for Wireless Sensor Networks”, IEEE Global High Tech Congress on Electronics, 2012.
[7] Dina S. Deif, Yasser Gadallah, “Classification of Wireless Sensor Networks Deployment Techniques”, IEEE Communications Surveys and Tutorials 16(2): 834-855 (2014).
[8] Fei Hu, Carter May, Xiaojun Cao.” Data Aggregation in Distributed Sensor Networks: Towards An Adaptive Timing Control”, Proceedings of the Third International Conference on Information Technology: New Generations (ITNG'06) ,IEEE 2006.
[9] Nakamura F, Loureiro A.F , and C. Frery, “Data Fusion for Wireless Sensor Networks: Techniques, Models, and Classifications,” ACM Computing Surveys, Vol. 39, No. 3, Article 9, 2007, August 2007.
[10] Ngo, T. C., Pham, T. M., & Oh, H. (2011). “An optimized message aggregation technique to resolve the funneling effect in mobility management”. In Proceedings of the 2011 international conference on selected topics in mobile and wireless networking (ICOST), 2011 (pp. 104–109).
[11] Qingwen Zhao and Yanmin Zhu, “An Efficient Data Aggregation Algorithm in Delay Tolerant Vehicular Networks”, Ad-hoc and Sensor Networking Symposium, IEEE ICC 2014.
[12] Taewoo ee, Dongsoo S. Kim, Hyunseung Choo and Mihui Kim. “A Delay-Aware Scheduling for Data Aggregation in Duty-Cycled Wireless Sensor Networks”, IEEE 9th International Conference on Mobile Adhoc and Sensor Networks, 2013.
[13] X. Xu, X. Li, X. Mao, S. Tang, and S. Wang, “A delay-efficient algorithm for data aggregation in multihop wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 1, pp. 163–175, Jan 2011.
[14] Yebin Chen, Jian Shu, Sheng Zhang, Linlan Liu, Limin Sun, “Data Fusion In Wireless Sensor Networks”, Second International Symposium on Electronic Commerce and Security,2009.