Abstract
Congestion in Wireless Sensor Networks is one of the primary causes of performance degradation due to severe packet loss leading to excessive energy consumption. Normally, the nodes closer to the sink are overburdened with huge traffic load as the data from the entire region are forwarded through them to reach the sink. As a result, their energy gets exhausted quickly and the network gets fragmented. To mitigate this issue, we propose a three phase energy balanced zone based routing protocol. Specifically, in the first phase the region is physically divided into equi-sized zones. In the second phase, a node with minimum traffic and minimum distance from other nodes inside each zone is selected as the zone leader. This leader is responsible for delivering data generated by any node in that zone and for routing and forwarding the data received from other zone leaders of the neighbor zones. In the third phase, zones are categorized as non-congested, medium-congested and congested zones and route path is established in inter and intra zones. The novelty of the proposed protocol lies behind the idea of incorporating the zone congestion levels along with the hop count into the routing decisions. A congestion control mechanism is proposed both at inter and intra zone levels in order to relieve the congested areas in case of congestion occurrence. Experimental evaluation shows that the proposed protocol has the potential to achieve up to 10.5% enhancement in the network throughput, 19.5% energy saving when compared with grid-based multi-path GMCAR with congestion avoidance and 23.03% energy saving when compared with priority-based application-specific congestion control clustering protocol PASCC.
Similar content being viewed by others
References
Chughtai, O., Badruddin, N., Awang, A., & Rehan, M. (2016). Congestion-aware and traffic load balancing scheme for routing in wsns. Telecommunications Systems, 61(1), 1–24.
Wan, C.-Y., Eisenman, S. B., & Campbell, A. T. (2011). Energy-efficient congestion detection and avoidance in sensor networks. ACM Transactions on Sensor Networks (TOSN), 7(4), 32.
Luo, C., Zhang, Y., & Xie, W. (2014). Traffic regulation based congestion control algorithm in sensor networks. Journal of Information Hiding and Multimedia Signal Processing, 5(2), 187–198.
Azharuddin, M., & Jana, P. K. (2015). A distributed algorithm for energy eficient and fault tolerant routing in wireless sensor networks. Wireless Networks, 21(1), 251–267.
Tao, L. Q., & Yu, F. Q. (2010). ECODA:enhanced congestion detection and avoidance for multiple class of traffic in sensor networks. IEEE Transactions on Consumer Electronics, 56(3), 1387–1394.
Fang, W., Chen, J., Shu, L., Chu, T., & Qian, D. (2010). Congestion avoidance, detection and alleviation in wirelesss sensor networks. Journal of Zhejiang University Science, 11(1), 63–73.
Karenos, K., Kalogeraki, V., & Krishnamurthy, S. V. (2005). Cluster-based congestion control for supporting multiple classes of traffic in sensor networks. In The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II, IEEE (pp. 107–114).
Chen, J. I.-Z., & Lin, C.-H. (2014). Throughput evaluation of a novel scheme to mitigate the congestion over WSNs. Wireless Personal Communications, 75(4), 1863–1877.
Rezaee, A. A., Yaghmaee, M. H., & Rahmani, A. M. (2014). Optimized congestion management protocol for healthcare wireless sensor networks. Wireless Personal Communications, 75(1), 11–34.
Dasgupta, R., Mukherjee, R., & Gupta, A. (2015). Congestion avoidance topology in wireless sensor network using Karnaugh map. In 2015 International Conference on Applications and Innovations in Mobile Computing (AIMoC), IEEE (pp. 89–96).
Zhang, N., Ding, N., & Hu, X. (2013). Congestion control based on multi-priority data for opportunistic routing. In Seventh China conference, CWSN, IEEE (pp. 122–132)
Chughtai, O., Badruddin, N., & Azlan, A. (2014). A congestion-aware and energy efficient traffic load balancing scheme for routing in WSNs. In TENCON 2014, IEEE (pp. 1–6).
Chitlange, M. M., & Deshpande, V. S. (2015). Effect of node density on congestion in WSN. In 2015 International Conference on Pervasive Computing, IEEE (pp. 1–3).
Banimelhem, O., & Khasawneh, S. (2012). GMCAR: Grid-based multipath with congestion avoidance routing protocol in wireless sensor networks. Ad Hoc Networks, 10(7), 1346–1361.
Thulasiraman, P., & White, K. A. (2016). Topology control of tactical wireless sensor networks using energy efficient zone routing. Digital Communications and Networks, 2(1), 1–14.
Zhou, Z. B., Chu, D., Shu, L., Hancke, G., Niu, J., & Ning, H. (2016). An energy-balanced heuristic for mobile sink scheduling in hybrid wsns. IEEE Transactions on Industrial Informatics, 12(1), 28–40.
Jannu, S., & Jana, P. K. (2015). A grid based clustering and routing algorithm for solving hot spot problem in wireless sensor networks. Wireless Networks, 22(6), 1901–1916.
Liu, W., Wang, Z., Zhang, S., & Wang, Q. (2010). A low power grid-based cluster routing algorithm of wireless sensor networks. Information Ttechnology and Applications (IFITA), IEEE, 1, 227–229.
Afsar, M., Tayarani-N, M.-H., & Aziz, M. (2016). An adaptive competition-based clustering approach for wireless sensor networks. Telecommunication Systems, 61(1), 181–204.
Liu, T., Li, Q., & Liang, P. (2012). An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Computer Communications, 35(17), 2150–2161.
Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET IET Wireless Sensor Systems, 4(1), 9–16.
Veena, K. N., & Vijaya Kumar, B. P. (2010). Dynamic clustering for wireless sensor networks: A neuro-fuzzy technique approach. In 2010 IEEE International Conference Computational Intelligence and Computing Research (ICCIC) (pp. 1–6).
Li, M., Jing, Y., & Li, C. (2014). A robust and efficient cross-layer optimal design in wireless sensor networks. Wireless Personal Communications, 72(4), 1889–1902.
Liu, Y., Xiong, N., Zhao, Y., Vasilaskos, A. V., Gao, J., & Jia, Y. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.
Heinzelman, W. R., Chandrashekaran, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawai International Conference in System sciences, 2000 (pp. 1–10).
Zhang, Z., & Cui, G. (2008). An effective congestion avoidance altering routing protocol in sensor networks. In 2008 International Conference on Computer Science and Software Engineering, IEEE (Vol. 4(4), pp. 980–983).
Kong, L., Xiang, Q., Liu, X., Liu, X.-Y., Gao, X., Chen, G., et al. (2016). ICP: Instantaneous clustering protocol for wireless sensor networks. Computer Networks, 101, 144–157.
Arafeh, B., Day, K., Touzene, A., & Alzeidi, N. (2014). Multipath grid-based enabled geographic routing for wireless sensor networks. Wireless Sensor Network, 6(12), 265–280.
Naznin, M., & Chowdhury, A. S. (2015). ZDG: Energy efficient zone based data gathering in a wireless sensor network. In International Conference in on Networking Systems and Security (NSysS), IEEE (pp. 1–7).
Jan, M. A., Nanda, P., He, X., & Liu, R. P. (2014). PASCC: Priority-based application-specific congestion control clustering protocol. Computer Networks, 74, 92–102.
Chi, Y.-P., & Chang, H.-P. (2013). An energy-aware grid-based routing scheme for wireless sensor networks. Telecommunication Systems, 54(4), 405–415.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sunitha, G.P., Dilip Kumar, S.M. & Vijaya Kumar, B.P. Energy Balanced Zone Based Routing Protocol to Mitigate Congestion in Wireless Sensor Networks. Wireless Pers Commun 97, 2683–2711 (2017). https://doi.org/10.1007/s11277-017-4630-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-017-4630-4