Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Optimizing routing based on congestion control for wireless sensor networks


Along with the increasing demands for the applications running on the wireless sensor network (WSN), energy consumption and congestion become two main problems to be resolved urgently. However, in most scenes, these two problems aren’t considered simultaneously. To address this issue, in this paper a solution that sufficiently maintains energy efficiency and congestion control for energy-harvesting WSNs is presented. We first construct a queuing network model to detect the congestion degree of nodes. Then with the help of the principle of flow rate in hydraulics, an optimizing routing algorithm based on congestion control (CCOR) is proposed. The CCOR algorithm is designed by constructing two functions named link gradient and traffic radius based on node locations and service rate of packets. Finally, the route selection probabilities for each path are allocated according to the link flow rates. The simulation results show that the proposed solution significantly decreases the packet loss rate and maintains high energy efficiency under different traffic load.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body area networks: A survey. Mobile Networks and Applications, 16(2), 171–193.

  2. 2.

    Liu, J., Wang, Q., Wan, J., X, J., & Zeng, B. (2013). Towards key issues of disaster aid based on wireless body area networks. KSII Transactions on Internet and Information Systems, 7(5), 1014–1035.

  3. 3.

    Xiang, L., Luo, J., & Vasilakos, A. (2011).Compressed data aggregation for energy efficient wireless sensor networks. In Proceedings of the 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON’11).

  4. 4.

    Vasilakos, A. V., Li, Z., Simon, G., & You, W. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10.

  5. 5.

    Wu, Y., & Liu, W. (2013). Routing protocol based on genetic algorithm for energy harvesting-wireless sensor networks. Wireless Sensor Systems, 3(2), 112–118.

  6. 6.

    Li, M., Li, Z., & Vasilakos, A. V. (2013). A Survey on topology control in wireless sensor networks, taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

  7. 7.

    Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

  8. 8.

    Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 294–312.

  9. 9.

    Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Review, 42(6), 1093–1102.

  10. 10.

    Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. Mobile Networks and Applications, 17(1), 4–20.

  11. 11.

    Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

  12. 12.

    Youssef, M., Ibrahim, M., Abdelatif, M., Chen, L., & Vasilakos, A. V. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

  13. 13.

    Dvir, A., & Vasilakos, A. V. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

  14. 14.

    Chen, M., Wan, J., Gonzalez, S., Liao, X., & Leung, V. C. M. (2014). A survey of recent developments in home M2M networks. IEEE Communications Surveys and Tutorials, 16(1), 98–114.

  15. 15.

    Sheng, Z., Yang, S., Yu, Y., Vasilakos, A. V., McCann, J. A., & Leung, K. K. (2013). A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98.

  16. 16.

    Busch, C., Kannan, R., & Vasilakos, A. V. (2012). Approximating congestion+ dilation in networks via “quality of routing” games. IEEE Trans. Computers, 61(9), 1270–1283.

  17. 17.

    Zawodniok, M., & Jagannathan, S. (2007). Predictive congestion control protocol for wireless sensor networks. IEEE Transactions on Wireless Communications, 6(11), 3955–3963.

  18. 18.

    Yaghmaee, M. H., & Adjeroh, D. A. (2009). Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks. Computer Networks, 53(11), 1798–1811.

  19. 19.

    Levis, P., Lee, N., Welsh, M., & Culler, D. (2003). TOSSIM: Accurate and scalable simulation of entire Tinyos applications. In Proceedings of the 1st international conference on Embedded networked sensor systems (SenSys’03).

  20. 20.

    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.

  21. 21.

    Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.

  22. 22.

    Yen, Y. S., Chao, H. C., Chang, R. S., & Vasilakos, A. V. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11–12), 2238–2250.

  23. 23.

    Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A Biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

  24. 24.

    Meng, T., Wu, F., Yang, Z., Chen, G., & Vasilakos, A. V. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers, PP(99), 1–13.

  25. 25.

    Li, P., Guo, S., & Vasilakos, A.V. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In Proceedings of IEEE INFOCOM.

  26. 26.

    Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

  27. 27.

    Wu, J., & Wang, Y. (2014). Hypercube-based multipath social feature routing in human contact networks. IEEE Transactions on Computers, 63(2), 383–396.

  28. 28.

    Azad, S., Casari, P., & Zorzi, M. (2014). Multipath routing with limited cross-path interference in underwater networks. IEEE Wireless Communications Letters, 3(5), 465–468.

  29. 29.

    Han, D., & Chung, J. M. (2014). Self-similar traffic end-to-end delay minimization multipath routing algorithm. IEEE Wireless Communications Letters, 18(12), 2121–2124.

  30. 30.

    Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D. Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for mac protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.

  31. 31.

    Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensor, 29(2), 334–342.

  32. 32.

    Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., & Gao, J. (2009). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

  33. 33.

    Gupta, P., & Kumar, P. (2000). The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2), 388–404.

  34. 34.

    Bisnik, N., & Abouzeid, A. (2009). Queuing network models for delay analysis of multihop wireless ad hoc networks. Ad Hoc Networks, 7(1), 79–97.

  35. 35.

    Burno, R., Conti, M., & Pinizzotto, A. (2009). A queuing modeling approach for load-aware route selection in heterogeneous mesh networks. IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops (WoWMoM 2009).

Download references


This work was supported by the National High Technology Research and Development of China (no. 2014AA01A701), Beijing Natural Science Foundation (4142049) and Fundamental Research Funds for the Central Universities (2015XS07).

Author information

Correspondence to Wei Ding.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ding, W., Tang, L. & Ji, S. Optimizing routing based on congestion control for wireless sensor networks. Wireless Netw 22, 915–925 (2016).

Download citation


  • Wireless sensor network
  • Congestion control
  • Flow rate
  • Optimizing routing