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Congestion-aware and traffic load balancing scheme for routing in WSNs

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Abstract

Congestion in a Wireless Sensor Network (WSN) is one of the causes of performance degradation due to severe packet loss that leads to excessive energy consumption. Solutions in WSNs try to avoid and overcome congestion by selecting sensor nodes with sufficient buffer space and adjusting the traffic rate at the source node over the shortest discovered route that usually decreases the End-to-End (ETE) throughput. On-demand routing protocols have the potential to discover the least congested route when it is required. In a WSN, most of the on-demand routing protocols replace the routing metric of the prevalent routing protocol with their proposed routing metric and keep the route discovery mechanism intact, which is not sufficient to increase the performance of the WSN. To address these problems, a novel Congestion-aware and Traffic Load balancing Scheme (CTLS) for routing has been proposed. The CTLS proactively avoids congestion through a novel route discovery mechanism to select the optimum node based on a composite metric. If congestion occurs, CTLS tries to detect it in a timely manner and alleviates it reactively using a novel ripple-based search approach. The simulation results show that the CTLS performs better as compared to the congestion avoidance, detection and alleviation and no congestion control schemes in terms of packet delivery ratio, ETE delay, throughput, and energy consumption per data packet in a resource constraint wireless network.

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References

  1. Agrawal, D., & Zeng, Q.-A. (2010). Introduction to wireless and mobile systems. Cengage Learning.

  2. Han, H., Zhang, B., & Li, Q. (2013). The improved routing protocol of wireless sensor network based on aodv. In Control conference (CCC), 2013 32nd Chinese (pp. 7410–7415). IEEE.

  3. Heo, J., Hong, J., & Cho, Y. (2009). Earq: Energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 5(1), 3–11.

    Article  Google Scholar 

  4. Kumar, M., Gupta, I., Tiwari, S., & Tripathi, R. (2013). A comparative study of reactive routing protocols for industrial wireless sensor networks. In Quality, reliability, security and robustness in heterogeneous networks (pp. 248–260). Berlin: Springer.

  5. Zhang, Z., & Zhou, H. (2009). Empirical examination of mobile ad hoc routing protocols on wireless sensor networks. International Journal of Computer Networks and Communications, 1(1), 75–87.

    Google Scholar 

  6. Booranawong, A., Teerapabkajorndet, W., & Limsakul, C. (2013). Energy consumption and control response evaluations of aodv routing in wsans for building-temperature control. Sensors, 13(7), 8303–8330.

    Article  Google Scholar 

  7. Kulik, J., Heinzelman, W., & Balakrishnan, H. (2002). Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Networks, 8(2/3), 169–185.

    Article  Google Scholar 

  8. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: a scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 56–67). ACM.

  9. Schurgers, C., & Srivastava, M.B. (2001). Energy efficient routing in wireless sensor networks. In Military communications conference, 2001. MILCOM 2001. Communications for network-centric operations: Creating the information force. IEEE (vol. 1, pp. 357–361). IEEE.

  10. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (pp. 10). IEEE.

  11. Johnson, D. B., Maltz, D. A., & Broch, J. (2001). Dsr: The dynamic source routing protocol for multi-hop wireless ad hoc networks. Pittsburgh, PA: Computer Science Department Carnegie Mellon University.

    Google Scholar 

  12. Perkins, C.E., & Royer, E.M. (1999). Ad-hoc on-demand distance vector routing. In Proceedings. WMCSA’99. Second IEEE workshop on mobile computing systems and applications (pp. 90–100). IEEE.

  13. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–28.

    Article  Google Scholar 

  14. Ee, C.T., & Bajcsy, R. (2004). Congestion control and fairness for many-to-one routing in sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 148–161). ACM.

  15. AlAmri, H., Abolhasan, M., Franklin, D. R., & Lipman, J. (2013). Optimised relay selection for route discovery in reactive routing. Ad Hoc Networks, 11(1), 70–88.

    Article  Google Scholar 

  16. Camara, D., & Loureiro, A.A.F. (2000). A gps/ant-like routing algorithm for ad hoc networks. In IEEE wireless communications and networking confernce WCNC, 2000 (vol. 3, pp. 1232–1236). IEEE.

  17. Azlan, A., Lagrange, X., & Ros, D. (2009). A cross-layer medium access control and routing protocol for wireless sensor networks. Proceedings of 10emes Journées Doctorales en Informatique et Réseaux (JDIR 2009).

  18. Fang, W., Chen, J., Shu, L., Chu, T., & Qian, D. (2010). Congestion avoidance, detection and alleviation in wireless sensor networks. Journal of Zhejiang University SCIENCE C, 11(1), 63–73.

    Article  Google Scholar 

  19. Wan, C. -Y., Eisenman, S. B., & Campbell, A. T. (2003). Coda: Congestion detection and avoidance in sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems (pp. 266–279). ACM.

  20. 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.

    Article  Google Scholar 

  21. Kumar, R., Crepaldi, R., Rowaihy, H., Harris, A. F., Cao, Guohong, Zorzi, Michele, et al. (2008). Mitigating performance degradation in congested sensor networks. IEEE Transactions on Mobile Computing, 7(6), 682–697.

    Article  Google Scholar 

  22. Ahmad, M. Z., & Turgut, D0. (2008). Congestion avoidance and fairness in wireless sensor networks. In Global telecommunications conference, 2008. IEEE GLOBECOM 2008 (pp. 1–6). IEEE.

  23. Fang, W.-W., Qian, D. P., & Liu, Y. (2008). Transmission control protocols for wireless sensor networks [j]. Journal of Software, 6, 020.

  24. Akan, O. B., & Akyildiz, I. F. (2005). Event-to-sink reliable transport in wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 13(5), 1003–1016.

    Article  Google Scholar 

  25. Lee, J.-H., & Jung, I.-B. (2010). Adaptive-compression based congestion control technique for wireless sensor networks. Sensors, 10(4), 2919–2945.

    Article  Google Scholar 

  26. Kang, J., Zhang, Y., & Nath, B. (2007). Tara: Topology-aware resource adaptation to alleviate congestion in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 18(7), 919–931.

    Article  Google Scholar 

  27. Puccinelli, D., & Haenggi, M. (2008). Arbutus: Network-layer load balancing for wireless sensor networks. In Wireless communications and networking conference, WCNC 2008, IEEE (pp. 2063–2068). IEEE.

  28. Ren, F., He, T., Das, S., & Lin, C. (2011). Traffic-aware dynamic routing to alleviate congestion in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(9), 1585–1599.

    Article  Google Scholar 

  29. Jea, D., Somasundara, A., & Srivastava, M. (2005). Multiple controlled mobile elements (data mules) for data collection in sensor networks. In Distributed computing in sensor systems (pp. 244–257). Berlin: Springer.

  30. Ye, F., Zhong, G., Lu, S., & Zhang, L. (2005). Gradient broadcast: A robust data delivery protocol for large scale sensor networks. Wireless Networks, 11(3), 285–298.

    Article  Google Scholar 

  31. Costa, L. H. M. K., Fdida, Serge, & Duarte, O. C. M. B. (2000). Distance-vector qos-based routing with three metrics. In Networking 2000 broadband communications, High Performance networking, and performance of communication networks (pp. 847–858). Berlin: Springer.

  32. Chughtai, O., Badruddin, N., & Awang, A. (2014). A congestion-aware and energy efficient traffic load balancing scheme for routing in wsns. In TENCON 2014-2014 IEEE region 10 conference (pp. 1–6). IEEE.

  33. Chughtai, O., Badruddin, N., & Awang, A. (2014). Distributed on-demand multi-optional routing protocol in multi-hop wireless networks. In TENCON 2014-2014 IEEE Region 10 conference (pp. 1–6). IEEE.

  34. Gupta, N., & Das, S. R. (2002). Energy-aware on-demand routing for mobile ad hoc networks. In Distributed computing (pp. 164–173). Berlin: Springer.

  35. Gouda, M. G., & Schneider, M. (2003). Maximizable routing metrics. IEEE/ACM Transactions on Networking (TON), 11(4), 663–675.

    Article  Google Scholar 

  36. Zhao, L., & Fan, C. (2004). Enhancement of qos differentiation over ieee 802.11 wlan. IEEE Communications Letters, 8(9), 494–496.

    Article  Google Scholar 

  37. Issariyakul, T., & Hossain, E. (2011). Introduction to network simulator NS2. Springer Science & Business Media.

  38. GS1011-datasheet. (2014). Low-power wireless system-on-chip wi-fi module data sheet, [online] available: Grainspan, http://www.datasheetarchive.com/gs1011-datasheet.html. accessed, May 2014.

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Acknowledgments

The authors would like to acknowledge the Ministry of Higher Education (MOHE) Malaysia, under the Fundamental Research Grant Scheme (FRGS/1/2012/TK02/UTP/03/02) fund and to the CISIR Laboratory, Universiti Teknologi PETRONAS for providing the research facility.

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Authors and Affiliations

  1. Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Tronoh, Malaysia

    Omer Chughtai, Nasreen Badruddin & Azlan Awang

  2. COMSATS Institute of Information Technology, Islamabad, Pakistan

    Omer Chughtai & Maaz Rehan

Authors
  1. Omer Chughtai
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  2. Nasreen Badruddin
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  3. Azlan Awang
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  4. Maaz Rehan
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Correspondence to Omer Chughtai.

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Chughtai, O., Badruddin, N., Awang, A. et al. Congestion-aware and traffic load balancing scheme for routing in WSNs. Telecommun Syst 63, 481–504 (2016). https://doi.org/10.1007/s11235-015-0126-2

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  • DOI: https://doi.org/10.1007/s11235-015-0126-2

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