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Clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads

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Abstract

With advancing vehicular technology, there are challenges related to the computing capabilities of the deployed infrastructure. In a dense vehicular network, the system performance quickly degrades due to the scarcity of computing capacity and the heavy workload on the coordinating nodes. In situations of a road accident or slow-moving traffic, many trigger messages are generated to enable situational awareness. Aforesaid events may lead to network congestion across the vehicular environment resulting in higher packet loss. Moreover, emergency messages incur increased service delays rather than getting preference for servicing. In this work, we propose a clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads. We use queue optimization to categorize and execute tasks based on their priority level at each fog node. Moreover, threshold-based congestion detection is used to determine congested nodes, which, alongside clustering-based suitable node selection, is used for workload sharing. Furthermore, a re-routing mechanism is implemented to decrease packet loss, improving the delivery rate. The simulation results show that the proposed technique is reliable, effective, and robust in terms of packet loss, throughput, and delivery of emergency messages.

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Availability of data and materials

The datasets used during the current study are publicly available from the public repository at https://github.com/IhabMoha/datasets-for-VANET.

Notes

  1. https://veins.car2x.org/.

  2. https://omnetpp.org/.

  3. https://sumo.com/

  4. https://github.com/IhabMoha/datasets-for-VANET.

References

  1. Ghazi MU, Khattak MAK, Shabir B, Malik AW, Ramzan MS (2020) Emergency message dissemination in vehicular networks: a review. IEEE Access 8:38606–38621

    Article  Google Scholar 

  2. Lobiyal D et al (2012) Performance evaluation of realistic vanet using traffic light scenario. Int J Wirel Mob Netw 4(1):237

    Article  Google Scholar 

  3. Karagiannis G, Altintas O, Ekici E, Heijenk G, Jarupan B, Lin K, Weil T (2011) Vehicular networking: a survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Commun Surv Tutor 13(4):584–616

    Article  Google Scholar 

  4. Qian Y, Moayeri N (2008) Design of secure and application-oriented vanets. In: Proceedings of the IEEE 67th Vehicular Technology Conference (VTC), IEEE, pp 2794–2799

  5. Sattari MRJ, Noor RM, Ghahremani S (2013) Dynamic congestion control algorithm for vehicular ad hoc networks. Int J Softw Eng Appl 7(3):95–108

    Google Scholar 

  6. Taleb M.Y, Merniz S, Harous S (2017) Congestion control techniques in vanets: A survey. In: Proceedings of the IEEE 13th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, pp 484–488

  7. Ahmad SA, Hajisami A, Krishnan H, Ahmed-Zaid F, Moradi-Pari E (2019) V2v system congestion control validation and performance. IEEE Trans Veh Technol 68(3):2102–2110

    Article  Google Scholar 

  8. Ravi B, Thangaraj J (2021) Stochastic traffic flow modeling for multi-hop cooperative data dissemination in vanets. Phys Commun 46:101290

    Article  Google Scholar 

  9. Jabbarpour MR, Noor RM, Khokhar RH, Ke C-H (2014) Cross-layer congestion control model for urban vehicular environments. J Netw Comput Appl 44:1–16

    Article  Google Scholar 

  10. Taherkhani N, Pierre S (2015) Improving dynamic and distributed congestion control in vehicular ad hoc networks. Ad Hoc Netw 33:112–125

    Article  Google Scholar 

  11. Jiang D, Chen Q, Delgrossi L (2007) Communication density: a channel load metric for vehicular communications research. In: Proceedings IEEE 4th International Conference on Mobile Adhoc and Sensor Systems (MASS), IEEE, pp 1–8

  12. Zang J, Towhidlou V, Shikh-Bahaei M-R (2020) A priority-based cross-layer design for future vanets through full-duplex technology. IEEE Trans Veh Technol

  13. Darus MY, Bakar KA (2011) Review of congestion control algorithm for event-driven safety messages in vehicular networks. Int J Comput Sci Issues 8(5):49

    Google Scholar 

  14. Wischhof L, Rohling H (2005) Congestion control in vehicular ad hoc networks. In: Proceedings IEEE International Conference on Vehicular Electronics and Safety, IEEE, pp 58–63

  15. Paranjothi A, Khan MS, Zeadally S (2020) A survey on congestion detection and control in connected vehicles. Ad Hoc Netw 108:102277

    Article  Google Scholar 

  16. Sahu P.K, Hafid A, Cherkaoui S (2014) Congestion control in vehicular networks using network coding. In: Proceedings of the IEEE International Conference on Communications (ICC), IEEE, pp 2736–2741

  17. Jang H-C, Feng W-C (2010) Network status detection-based dynamic adaptation of contention window in IEEE 802.11 p. In: Proceedings IEEE 71st Vehicular Technology Conference (VTC), IEEE, pp 1–5

  18. Zemouri S, Djahel S, Murphy J (2014) Smart adaptation of beacons transmission rate and power for enhanced vehicular awareness in vanets. In: Proceedings IEEE 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE, pp 739–746

  19. Wang X, Ning Z, Wang L (2018) Offloading in internet of vehicles: a fog-enabled real-time traffic management system. IEEE Trans Ind Inform 14(10):4568–4578

    Article  Google Scholar 

  20. Hou X, Ren Z, Wang J, Cheng W, Ren Y, Chen K-C, Zhang H (2020) Reliable computation offloading for edge-computing-enabled software-defined IoV. IEEE Internet Things J 7(8):7097–7111

    Article  Google Scholar 

  21. Tang C, Wei X, Zhu C, Wang Y, Jia W (2020) Mobile vehicles as fog nodes for latency optimization in smart cities. IEEE Trans Veh Technol 69(9):9364–9375

    Article  Google Scholar 

  22. Zhang K, Mao Y, Leng S, He Y, Zhang Y (2017) Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading. IEEE Veh Technol Mag 12(2):36–44

    Article  Google Scholar 

  23. Alkhalifa IS, Almogren AS (2020) NSSC: novel segment based safety message broadcasting in cluster-based vehicular sensor network. IEEE Access 8:34299–34312

    Article  Google Scholar 

  24. Karpagalakshmi R, Vijayalakshmi P, Gowsic K, Rathi R (2021) An effective traffic management system using connected dominating set forwarding (CDSF) framework for reducing traffic congestion in high density vanets. Wirel Pers Commun 119(3):2725–2754

    Article  Google Scholar 

  25. Gomides TS, Robson E, Meneguette RI, de Souza FS, Guidoni DL (2022) Predictive congestion control based on collaborative information sharing for vehicular ad hoc networks. Comput Netw 211:108955

    Article  Google Scholar 

  26. Falahatraftar F, Pierre S, Chamberland S (2022) An intelligent congestion avoidance mechanism based on generalized regression neural network for heterogeneous vehicular networks. IEEE Transactions on Intelligent Vehicles

  27. Li W, Song W, Lu Q, Yue C (2020) Reliable congestion control mechanism for safety applications in urban vanets. Ad Hoc Netw 98:102033

    Article  Google Scholar 

  28. Peixoto MLM, Maia AH, Mota E, Rangel E, Costa DG, Turgut D, Villas LA (2021) A traffic data clustering framework based on fog computing for vanets. Veh Communi 31:100370

    Google Scholar 

  29. Chaqfeh M, Lakas A, Jawhar I (2014) A survey on data dissemination in vehicular ad hoc networks. Veh Commun 1(4):214–225

    Google Scholar 

  30. Ahmed E, Ahmed A, Yaqoob I, Shuja J, Gani A, Imran M, Shoaib M (2017) Bringing computation closer toward the user network: Is edge computing the solution? IEEE Commun Mag 55(11):138–144

    Article  Google Scholar 

  31. Malarvizhi K, Jayashree L (2021) Dynamic scheduling and congestion control for minimizing delay in multihop wireless networks. J Ambient Intell Hum Comput 12(3):3949–3957

    Article  Google Scholar 

  32. Taherkhani N, Pierre S (2016) Centralized and localized data congestion control strategy for vehicular ad hoc networks using a machine learning clustering algorithm. IEEE Trans Intell Transp Syst 17(11):3275–3285

    Article  Google Scholar 

  33. Bai S, Oh J, Jung J-I (2013) Context awareness beacon scheduling scheme for congestion control in vehicle to vehicle safety communication. Ad Hoc Netw 11(7):2049–2058

    Article  Google Scholar 

  34. Campolo C, Molinaro A, Vinel A, Zhang Y (2011) Modeling prioritized broadcasting in multichannel vehicular networks. IEEE Trans Veh Technol 61(2):687–701

    Article  Google Scholar 

  35. Abedi O, Berangi R, Azgomi M.A (2009) Improving route stability and overhead on aodv routing protocol and make it usable for vanet. In: Proceedings IEEE 29th International Conference on Distributed Computing Systems Workshops (ICDCS), IEEE, pp 464–467

  36. Guan W, He J, Bai L, Tang Z (2011) Adaptive congestion control of DSRC vehicle networks for collaborative road safety applications. In: Proceedings IEEE 36th Conference on Local Computer Networks, IEEE, pp 913–917

  37. Dahiya A, Chauhan R, Kait R (2014) Experimental analysis of AODV and LBV_AODV with varying vehicle mobility in vehicular scenario. In: Proceedings IEEE 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), IEEE, pp 1–4

  38. Autolitano A, Campolo C, Molinaro A, Scopigno R.M, Vesco A (2013) An insight into decentralized congestion control techniques for VANETs from ETSI TS 102 687 v1. 1.1. In: Proceedings IEEE 1st IFIP Wireless Days (WD), IEEE, pp 1–6

  39. Ali G.M.N, Mollah M.A.S, Samantha S.K, Mahmud S (2014) An efficient cooperative load balancing approach in rsu-based vehicular ad hoc networks (vanets). In: Proceedings IEEE 4th International Conference on Control System, Computing and Engineering (ICCSCE), IEEE, pp 52–57

  40. Zhang X, Cao X, Yan L, Sung DK (2015) A street-centric opportunistic routing protocol based on link correlation for urban vanets. IEEE Trans Mob Comput 15(7):1586–1599

    Article  Google Scholar 

  41. Bracciale L, Loreti P (2020) Lyapunov drift-plus-penalty optimization for queues with finite capacity. IEEE Commun Lett

  42. Ali M, Malik AW, Rahman AU, Iqbal S, Hamayun MM (2019) Position-based emergency message dissemination for internet of vehicles. Int J Distrib Sens Netw 15(7):1550147719861585

    Article  Google Scholar 

  43. Tang Y, Cheng N, Wu W, Wang M, Dai Y, Shen X (2019) Delay-minimization routing for heterogeneous vanets with machine learning based mobility prediction. IEEE Trans Veh Technol 68(4):3967–3979

    Article  Google Scholar 

  44. Zang Y, Stibor L, Cheng X, Reumerman H.-J, Paruzel A, Barroso A (2007) Congestion control in wireless networks for vehicular safety applications. In: Proceedings of the 13th European Wireless Conference (EW), vol 7, p 1

  45. IEEE Computer Society LAN MAN Standard Committee (1999) Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. ANSI/IEEE Std. 802.11-1999

  46. Torrent-Moreno M, Santi P, Hartenstein H (2006) Distributed fair transmit power adjustment for vehicular ad hoc networks. In: Proceedings IEEE 3rd Annual Communications Society on Sensor and Ad Hoc Communications and Networks (SECON), IEEE, vol 2, pp 479–488

  47. Shah SS, Ali M, Malik AW, Khan MA, Ravana SD (2019) vfog: a vehicle-assisted computing framework for delay-sensitive applications in smart cities. IEEE Access 7:34900–34909

    Article  Google Scholar 

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Funding

The authors have not received any funding.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan

    Asad Waqar Malik & Anis Ur Rahman

  2. Lahore University of Management Science, Lahore, 54792, Pakistan

    Muhammad Ali

  3. Intelligent Systems Center, Missouri Science and Technology, Rolla, 65409, US

    Asad Waqar Malik

  4. Faculty of Mathematics and Science, University of Jyväskylä, Jyväskylä, Finland

    Anis Ur Rahman

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  1. Muhammad Ali
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Contributions

Ali and Malik have presented the Idea. Ali has performed the implementation. Rahman has built the machine learning model and helped to optimize it for better results, whereas Malik has performed the evaluation. For the write-up, Ali has written the initial draft. Malik and Rahman have improved the write-up to the final submission. All authors reviewed the manuscript.

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Correspondence to Asad Waqar Malik.

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Ali, M., Malik, A.W. & Rahman, A.U. Clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads. J Supercomput 79, 21144–21165 (2023). https://doi.org/10.1007/s11227-023-05455-1

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