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Congestion avoidance in wireless sensor network using software defined network

Abstract

Wireless sensor network (WSN) is a core component of multiple smart city applications. Utilizing the same WSN for multiple applications helps reduce cost. However, satisfying quality of service requirements of these independent applications is very challenging. For instance, uncoordinated path selection for data dissemination may result in the formation of queues in the WSN violating end-to-end delay requirements of several applications. To this end, we propose a software defined network based approach to ensure satisfaction of individual delay constraints while ensuring minimal increase in the average queue length of the WSN. The approach utilizes a logically centralized controller to generate a comprehensive view of the whole network in a scalable manner. We develop several graph theoretic algorithms to reduce the number of nodes and edges in the communication paths and to identify the most suitable communication paths for each application so that end-to-end delays are minimized. The evaluations demonstrate that our approach performs up to 34% better than existing works and up to 14% worst in comparison to the optimal solution for different topologies, network sizes, and end-to-end delay requirements. Moreover, performance of the proposed graph theoretic algorithms is also measured w.r.t. time.

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Notes

  1. 1.

    https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html, 68% of human population will be living in urban areas.

  2. 2.

    https://economictimes.indiatimes.com/news/economy/infrastructure/smart-city-mission-5151-projects-at-various-stages-of-implementation-says-survey/articleshow/73802323.cms.

References

  1. 1.

    Al-Shammari BKJ, Al-Aboody N, Al-Raweshidy HS (2017) IoT traffic management and integration in the QoS supported network. IEEE Internet Things J 5(1):352–370

    Article  Google Scholar 

  2. 2.

    Abdelmoniem AM, Bensaou B (2016) SDN-based generic congestion control mechanism for data centers: implementation and evaluation. Dept. Comput. Sci. Eng., Univ. Sci. Technol., Hong Kong, Tech. Rep. HKUST-CS16-02

  3. 3.

    Abdelmoniem AM, Bensaou B (2016) SDN-based incast congestion control framework for data centers: implementation and evaluation. CSE Dept, HKUST, Tech. Rep. HKUST-CS16-01

  4. 4.

    Abidoye AP (2018) Modelling and QoS implementation of wireless sensor networks based on the ant colony optimization approach

  5. 5.

    Ahad MA et al (2020) Enabling technologies and sustainable smart cities. Sustain Cities Soc 61:102301

    Article  Google Scholar 

  6. 6.

    Ai J et al (2019) Improving resiliency of software-defined networks with network coding-based multipath routing. In: 2019 IEEE Symposium on Computers and Communications, ISCC 2019, Barcelona, Spain, June 29–July 3, 2019. IEEE, pp 1–6. https://doi.org/10.1109/ISCC47284.2019.8969591

  7. 7.

    Akyildiz IF et al (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114

    Article  Google Scholar 

  8. 8.

    Alfoudi ASD et al (2019) Seamless mobility management in heterogeneous 5G networks: a coordination approach among distributed SDN controllers. In: 89th IEEE Vehicular Technology Conference, VTC Spring 2019, Kuala Lumpur, Malaysia, April 28–May 1, 2019. IEEE, pp 1–6. https://doi.org/10.1109/VTCSpring.2019.8746712

  9. 9.

    Alghamdi TA (2020) Route optimization to improve QoS in multi-hop wireless sensor networks. Wirel Netw 1–7

  10. 10.

    Alves RCA et al (2017) IT-SDN: improved architecture for SDWSN. In: XXXV Brazilian symposium on computer networks and distributed systems

  11. 11.

    Alwan H, Agarwal A (2013) Multi-objective QoS routing for wireless sensor networks. In: 2013 International Conference on Computing, Networking and Communications (ICNC). IEEE, pp 1074–1079

  12. 12.

    Bera S et al (2016) Soft-WSN: software-defined WSN management system for IoT applications. IEEE Syst J 12(3):2074–2081

    Article  Google Scholar 

  13. 13.

    Bhowmik S et al (2016) High performance publish/subscribe middleware in software-defined networks. IEEE/ACM Trans Netw 25(3):1501–1516

    Article  Google Scholar 

  14. 14.

    Bhowmik S et al (2016) Hybrid content-based routing using network and application layer filtering. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp 221–231

  15. 15.

    Chinnappen-Rimer S, Hancke GP (2009) Modelling a wireless sensor network as a small world network. In: 2009 international conference on wireless networks and information systems. IEEE, pp 7–10

  16. 16.

    Costanzo S et al (2012) Software defined wireless networks: unbridling SDNs. In: 2012 European Workshop on Software Defined Networking (EWSDN). IEEE, pp 1–6

  17. 17.

    Das K, Samanta S, Pal M (2018) Study on centrality measures in social networks: a survey. Soc Netw Anal Min 8(1):1–11

    Article  Google Scholar 

  18. 18.

    Deepa O, Suguna J (2020) An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks. J King Saud Univ Comput Inf Sci 32(7):763–774

    Google Scholar 

  19. 19.

    Elangovan G, Kumanan T (2020) Congestion aware adaptive reverse routing strategy for improving QoS in WSN. In: IOP conference series: materials science and engineering, vol 925. 1. IOP Publishing, p 012069

  20. 20.

    Farhady H, Lee HY, Nakao A (2015) Software-defined networking: a survey. Comput Netw 81:79–95

    Google Scholar 

  21. 21.

    Farias CMD et al (2016) A systematic review of shared sensor networks. ACM Comput Surv (CSUR) 48(4):51

    Article  Google Scholar 

  22. 22.

    Frey H, Rührup S, Stojmenović I (2009) Routing in wireless sensor networks’. In: Guide to wireless sensor networks. Springer, pp 81–111

  23. 23.

    Galluccio L et al (2015) SDN-WISE: design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, pp 513–521

  24. 24.

    Guidoni DL, Mini RAF, Loureiro AAF (2008) Creating small-world models in wireless sensor networks. In: 2008 IEEE 19th international symposium on personal, indoor and mobile radio communications. IEEE, pp 1–6

  25. 25.

    Hu T, Guo Z, Yi P, Baker T, Lan J (2018) Multi-controller based software-defined networking: a survey. IEEE Access 6:15980–15996

  26. 26.

    Iwendi C et al (2020) A metaheuristic optimization approach for energy efficiency in the IoT networks. Softw Pract Exp

  27. 27.

    Jereczek G et al (2015) A lossless switch for data acquisition networks. In: 2015 IEEE 40th conference on Local Computer Networks (LCN), pp 552–560

  28. 28.

    Kamarei M et al (2020) SiMple: a unified single and multi-path routing algorithm for wireless sensor networks with source location privacy. IEEE Access 8:33818–33829

    Article  Google Scholar 

  29. 29.

    Kanagevlu R, Aung KMM (2015) SDN controlled local rerouting to reduce congestion in cloud data center. In: 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI), pp 80–88

  30. 30.

    Karenos K, Kalogeraki V (2007) Facilitating congestion avoidance in sensor networks with a mobile sink. In: 28th IEEE International Real-Time Systems Symposium (RTSS 2007). IEEE, pp 321–332

  31. 31.

    Kaur T, Kumar D (2020) MACO-QCR: multi-objective ACO based QoS-aware cross-layer routing protocols in WSN. IEEE Sens J 21(5):6775–6783

    Article  Google Scholar 

  32. 32.

    Kobo HI, Abu-Mahfouz AM, Hancke GP (2017) A survey on software-defined wireless sensor networks: challenges and design requirements. IEEE Access 5(1):1872–1899

    Article  Google Scholar 

  33. 33.

    Kreutz D et al (2015) Software-defined networking: a comprehensive survey. Proc IEEE 103(1):14–76

    Article  Google Scholar 

  34. 34.

    Kulkarni A, Sathe S (2014) Healthcare applications of the Internet of Things. A review. Int J Comput Sci Inf Technol 5(5):6229–6232

  35. 35.

    Letswamotse BB et al (2018) Software defined wireless sensor networks and efficient congestion control. IET Netw 7(6):460–464

    Article  Google Scholar 

  36. 36.

    Little JDC (1961) A proof for the queuing formula: \({\text{ L }}= lambda \) W. Oper Res 9(3):383–387

    Article  Google Scholar 

  37. 37.

    Maheswari U (2018) A survey on recent techniques for energy efficient routing in WSN. Int J Sens Sens Netw 6(1):8

    Article  Google Scholar 

  38. 38.

    Modieginyane KM et al (2018) Software defined wireless sensor networks application opportunities for efficient network management: a survey. Comput Electr Eng 66:274–287

    Article  Google Scholar 

  39. 39.

    Mundada MR, Desai PB et al (2016) A survey of congestion in wireless sensor networks. In: 2016 international conference on advances in Human Machine Interaction (HMI). IEEE, pp 1–5

  40. 40.

    Narawade V, Kolekar UD (2018) ACSRO: adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks. Alex Eng J 57(1):131–145

    Article  Google Scholar 

  41. 41.

    Narawade VE, Kolekar UD (2016) Congestion avoidance and control in wireless sensor networks: a survey. In: 2016 International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, pp 1–5

  42. 42.

    Nayak NG, Dürr F, Rothermel K (2017) Incremental flow scheduling and routing in time-sensitive software-defined networks. IEEE Trans Ind Inform 14(5):2066–2075

    Article  Google Scholar 

  43. 43.

    Razaque A et al (2016) P-LEACH: energy efficient routing protocol for wireless sensor networks. In: 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, pp 1–5

  44. 44.

    Sangaiah AK et al (2019) Enforcing position-based confidentiality with machine learning paradigm through mobile edge computing in realtime industrial informatics. IEEE Trans Ind Inform 15(7):4189–4196

    MathSciNet  Article  Google Scholar 

  45. 45.

    Sangaiah AK et al (2020) LACCVoV: linear adaptive congestion control with optimization of data dissemination model in vehicle-to vehicle communication. IEEE Trans Intell Transp Syst

  46. 46.

    Shah SA, Nazir B, Khan IA (2017) Congestion control algorithms in wireless sensor networks: trends and opportunities. J King Saud Univ Comput Inf Sci 29(3):236–245

    Google Scholar 

  47. 47.

    Shin SW et al (2013) Fresco: modular composable security services for software-defined networks. In: 20th annual Network & Distributed System Security Symposium: NDSS

  48. 48.

    Steffan J et al (2005) Towards multi-purpose wireless sensor networks. In: 2005 Systems Communications (ICW’05, ICHSN’05, ICMCS’05, SENET’05). IEEE, pp 336–341

  49. 49.

    Sujanthi S, Kalyani SN (2020) SecDL: QoS-aware secure deep learning approach for dynamic cluster-based routing in WSN assisted IoT. Wirel Pers Commun 114(3):2135–2169

    Article  Google Scholar 

  50. 50.

    Suma S, Harsoor B (2019) Congestion control algorithms for traffic and resource control in wireless sensor networks. In: International conference on emerging trends in engineering. Springer, pp 750–758

  51. 51.

    Tariq N et al (2019) A mobile code-driven trust mechanism for detecting internal attacks in sensor node-powered IoT. J Parallel Distrib Comput 134:198–206

    Article  Google Scholar 

  52. 52.

    Vinodhini R, Gomathy C (2019) A hybrid approach for energy efficient routing in WSN: using DA and GSO algorithms. In: International conference on inventive computation technologies. Springer, pp 506–522

  53. 53.

    Wang J et al (2017) A software defined network routing in wireless multihop network. J Netw Comput Appl 85:76–83

    Article  Google Scholar 

  54. 54.

    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘smallworld’ networks. Nature 393(6684):440

  55. 55.

    Xu C et al (2019) An energy-efficient region source routing protocol for lifetime maximization in WSN. IEEE Access 7:135277–135289

    Article  Google Scholar 

  56. 56.

    Yadav SL, Ujjwal RL (2020) Sensor data fusion and clustering: a congestion detection and avoidance approach in wireless sensor networks. J Inf Optim Sci 41(7):1673–1688

    Google Scholar 

  57. 57.

    Yadav SL et al (2021) Traffic and energy aware optimization for congestion control in next generation wireless sensor networks. J Sens, Hindawi 2021:5575802. https://doi.org/10.1155/2021/5575802

  58. 58.

    Yu C et al (2019) An adaptive and lightweight update mechanism for SDN. IEEE Access 7:12914–12927. https://doi.org/10.1109/ACCESS.2019.2893058

    Article  Google Scholar 

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Correspondence to Thar Baker.

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Khan, A.N., Tariq, M.A., Asim, M. et al. Congestion avoidance in wireless sensor network using software defined network. Computing 103, 2573–2596 (2021). https://doi.org/10.1007/s00607-021-01010-z

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Keywords

  • Congestion control
  • Software defined networking
  • Smart city
  • Wireless sensor network
  • Quality of service

Mathematics Subject Classification

  • 68M10