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On Minimizing TCP Traffic Congestion in Vehicular Internet of Things (VIoT)

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Abstract

The performance of end-to-end wireless link congestion control algorithm in the vehicular internet of things network is plagued by the inherent limitations of spurious rate control initiation, slow convergence time, and fairness disparity. In this article, the delay assisted rate tuning (DART) approach is proposed for the vehicular network that implements two algorithms, utilization assisted reduction (UAR) and super linear convergence (SLC), to overcome the transmission control protocol (TCP) limitations. The UAR algorithm is responsible for initiating the proportionate rate control process based on the bottleneck prediction parameter, thereby regulating the needless rate control during non-congested losses. In the congestion recovery mode, the SLC algorithm executes a dynamic rate update mechanism that enhances the flow rate and minimizes bandwidth sharing disparity among TCP flows. An analytical model was developed to study the DART convergence rate and fairness performance against the existing algorithm. The vehicular simulation outcome also confirms significant enhancement in average transmission rate, average message latency, and average bandwidth sharing performances of the DART algorithms against the RFC 6582, TCP-LoRaD, and CERL + congestion avoidance algorithms under varying traffic flows and node movement scenarios.

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Data Availability

The datasets analyzed during the current study are not publicly available, compromising our future research programs. Still, they are available from the corresponding author on reasonable request.

References

  1. Ji, B., Zhang, X., Mumtaz, S., Han, C., Li, C., Wen, H., & Wang, D. (2020). Survey on the internet of vehicles: Network architectures and applications. IEEE Communications Magazine, 4(1), 34–41. https://doi.org/10.1007/978-981-10-3376-6_48

    Article  Google Scholar 

  2. Manojkumar, P., Suresh, M., Ahmed, AAA., Panchal, H., Rajan, CA., Dheepanchakkravarthy, A., Geetha, A., Gunapriya, B., Mann, S., Sadasivuni, KK. (2021) A novel home automation distributed server management system using Internet of Things International Journal of Ambient Energyhttps://doi.org/10.1080/01430750.2021.1953590

  3. Kaushik, S., Srinivasan, K., Sharmila, B., Devasena, D., Suresh, M., Panchal, H., Ashokkumar, R., Sadasivuni, KK., & Srimali, N. (2021). Continuous monitoring of power consumption in urban buildings based on Internet of Things. International Journal of Ambient Energy, 1https://doi.org/10.1080/01430750.2021.1931961

  4. Rajamoorthy, R., Arunachalam, G., Kasinathan, P., Ramkumar Devendiran, P., Ahmadi, S. P., Muthusamy, S., Panchal, H., Kazem, H. A., & Sharma, P. (2022). A novel intelligent transport system charging scheduling for electric vehicles using grey wolf optimizer and sail fish optimization algorithms. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(2), 3555–3575. https://doi.org/10.1080/15567036.2022.2067268

    Article  Google Scholar 

  5. Lu, H., Liu, Q., Tian, D., Li, Y., Kim, H., & Serikawa, S. (2019). The cognitive internet of vehicles for autonomous driving. IEEE Network, 33, 65–73. https://doi.org/10.1109/MNET.2019.1800339

    Article  Google Scholar 

  6. Arena, F., Pau, G., & Severino, A. (2020). A review on IEEE 802.11 p for intelligent transportation systems. Journal of Sensor and Actuator Networks, 9(2), 22. https://doi.org/10.3390/jsan9020022

    Article  Google Scholar 

  7. Kaffash, S., Nguyen, A. T., & Zhu, J. (2021). Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis. International Journal of Production Economics, 231, 107868. https://doi.org/10.1016/j.ijpe.2020.107868

    Article  Google Scholar 

  8. Lin, Y., Wang, P., Ma, M. (2017) Intelligent transportation system (ITS): Concept, challenge and opportunity, In: Proc.-3rd IEEE Int. Conf. Big Data Secur. (pp. 167–172). https://doi.org/10.1109/BigDataSecurity.2017.50.

  9. Thompson, A. W., & Perez, Y. (2020). Vehicle-to-Everything (V2X) energy services, value streams, and regulatory policy implications. Energy Policy, 137, 111–136. https://doi.org/10.1016/j.enpol.2019.111136

    Article  Google Scholar 

  10. Moubayed, A., Shami, A., Heidari, P., Larabi, A., & Brunner, R. (2020). Edge-enabled V2X service placement for intelligent transportation systems. IEEE Transactions on Mobile Computing, 20(4), 1380–1392. https://doi.org/10.1109/TMC.2020.2965929

    Article  Google Scholar 

  11. Abdel Hakeem, S. A., Hady, A. A., & Kim, H. (2020). Current and future developments to improve 5G-newradio performance in vehicle-to-everything communications. Telecommunication Systems, 75(3), 331–353. https://doi.org/10.1007/s11235-020-00704-7

    Article  Google Scholar 

  12. Laña, I., Sanchez-Medina, J. J., Vlahogianni, E. I., & Del Ser, J. (2021). From data to actions in intelligent transportation systems: A prescription of functional requirements for model actionability. Sensors, 21(4), 1121. https://doi.org/10.3390/s21041121

    Article  Google Scholar 

  13. Mir, Z. H., Toutouh, J., Filali, F., & Ko, Y. B. (2020). Enabling DSRC and C-V2X integrated hybrid vehicular networks: Architecture and protocol. IEEE access, 8, 180909–180927. https://doi.org/10.1109/ACCESS.2020.3027074

    Article  Google Scholar 

  14. Silva, L., Magaia, N., Sousa, B., Kobusińska, A., Casimiro, A., Mavromoustakis, C. X., & De Albuquerque, V. H. C. (2021). Computing paradigms in emerging vehicular environments: A review. IEEE/CAA Journal of Automatica Sinica, 8(3), 491–511. https://doi.org/10.1109/JAS.2021.1003862

    Article  Google Scholar 

  15. Sadovaya, Y., & Zavjalov, S. V. (2020). Dedicated short-range communications: Performance evaluation over mmWave and potential adjustments. IEEE Communications Magazine, 24(12), 2733–2736. https://doi.org/10.1109/LCOMM.2020.3016634

    Article  Google Scholar 

  16. SenthamilSelvan, R., Wahidabanu, R. S. D., & Karthik, B. (2022). Intersection collision avoidance in dedicated short-range communication using vehicle ad hoc network. Concurrency and Computation, 34(13), e5856. https://doi.org/10.1002/cpe.5856

    Article  Google Scholar 

  17. Vega, C., Roquero, P., & Aracil, J. (2017). Multi-Gbps HTTP traffic analysis in commodity hardware based on local knowledge of TCP streams. Computer Networks, 113, 258–268. https://doi.org/10.1016/j.comnet.2017.01.001

    Article  Google Scholar 

  18. Murray, D., Koziniec, T., Zander, S., Dixon, M., Koutsakis, P. (2018) An analysis of changing enterprise network traffic characteristics, In: 2017 23rd Asia-Pacific Conf. Commun. Bridg. Metrop. Remote. APCC 2017. 2018-January (pp.1–6). https://doi.org/10.23919/APCC.2017.8303960.

  19. Zhang, T., Wang, J., Huang, J., Chen, J., Pan, Y., & Min, G. (2017). Tuning the aggressive TCP behavior for highly concurrent HTTP connections in intra-datacenter. IEEE/ACM Transactions on Networking, 25, 3808–3822. https://doi.org/10.1109/TNET.2017.2759300

    Article  Google Scholar 

  20. Kogias, M., & Bugnion, E. (2018). Flow control for latency-critical rpcs. In: Proceedings of the 2018 Afternoon Workshop on Kernel Bypassing Networks (pp. 15-21). https://doi.org/10.1145/3229538.3229541

  21. Kanellopoulos, D. (2019). Congestion control for MANETs: An overview. ICT Express, 5, 77–83. https://doi.org/10.1016/j.icte.2018.06.001

    Article  Google Scholar 

  22. Henderson, T., Floyd, S., Gurtov, A., Nishida, Y. (2012) RFC 6582: The NewReno modification to TCP’s fast recovery algorithm, Rfc. (pp. 1689–1699). https://doi.org/10.17487/RFC6582

  23. Molia, H. K., & Kothari, A. D. (2018). TCP variants for mobile adhoc networks: Challenges and solutions. Wireless Personal Communications, 100, 1791–1836. https://doi.org/10.1007/s11277-018-5675-8

    Article  Google Scholar 

  24. Saedi, T., & El-Ocla, H. (2021). TCP CERL+: Revisiting TCP congestion control in wireless networks with random loss. Wireless Networks, 27, 423–440. https://doi.org/10.1007/s11276-020-02459-0

    Article  Google Scholar 

  25. Abdelsalam, A., Luglio, M., Roseti, C., Zampognaro, F., & Wave, T. C. P. (2017). A new reliable transport approach for future internet. Computer Networks, 112, 122–143. https://doi.org/10.1016/j.comnet.2016.11.002

    Article  Google Scholar 

  26. Wang, J., Wen, J., Zhang, J., Xiong, Z., & Han, Y. (2016). TCP-FIT: An improved TCP algorithm for heterogeneous networks. Journal of Network and Computer Applications, 71, 167–180. https://doi.org/10.1016/j.jnca.2016.03.020

    Article  Google Scholar 

  27. Sarkar, N. I., Ho, P. H., Gul, S., & Zabir, S. M. S. (2022). TCP-LoRaD: A loss recovery and differentiation algorithm for improving TCP performance over MANETs in noisy channels. Electronics, 11(9), 1479. https://doi.org/10.3390/electronics11091479

    Article  Google Scholar 

  28. Zhang, J., Wen, J., & Han, Y. (2017). TCP-ACC: Performance and analysis of an active congestion control algorithm for heterogeneous networks. Frontiers of Computer Science, 11, 1061–1074. https://doi.org/10.1007/s11704-016-5482-x

    Article  Google Scholar 

  29. Brakmo, L. S., Peterson, L. L., & Vegas, T. C. P. (1995). End to end congestion avoidance on a global internet. IEEE Journal on Selected Areas in Communications, 13, 1465–1480. https://doi.org/10.1109/49.464716

    Article  Google Scholar 

  30. Guan, S., Jiang, Y., & Guan, Q. (2020). Improvement of TCP Vegas algorithm based on forward direction delay. International Journal of Web Engineering and Technology, 15(1), 81–95. https://doi.org/10.1504/IJWET.2020.107690

    Article  Google Scholar 

  31. Jamali, S., Alipasandi, N., Alipasandi, B., & Pegas, T. C. P. (2015). A PSO-based improvement over TCP Vegas. Applied. Soft Computing, 32, 164–174. https://doi.org/10.1016/j.asoc.2015.03.048

    Article  Google Scholar 

  32. Jiang, H., Luo, Y., Zhang, Q. Y., Yin, M. Y., & Wu, C. (2017). TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks, Wirel. Networks, 23, 1535–1548. https://doi.org/10.1007/s11276-016-1242-y

    Article  Google Scholar 

  33. Sunitha, D., Nagaraju, A., & Narsimha, G. (2017). Dynamic TCP-Vegas based on cuckoo search for efficient congestion control for MANET. International. Journal of Signal Imaging Systems Engineering, 10, 47–53. https://doi.org/10.1504/IJSISE.2017.084570

    Article  Google Scholar 

  34. Janowski, R., Grabowski, M., & Arabas, P. (2020). New heuristics for TCP retransmission timers. Progress in computer recognition systems (pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-030-19738-4_13

    Chapter  Google Scholar 

  35. Zhao, Y., Cheng, G., Zhang, W., Chen, X., & Li, J. (2020). Bctcp: A feedback-based congestion control method. China Communications, 17(6), 13–25. https://doi.org/10.23919/JCC.2020.06.002

    Article  Google Scholar 

  36. de Carvalho, J. A., da Costa, D. B., Yang, L., Alexandropoulos, G. C., Oliveira, R., & Dias, U. S. (2020). User fairness in wireless powered communication networks with non-orthogonal multiple access. IEEE Wireless Communication Letters, 10(1), 189–193. https://doi.org/10.1109/LWC.2020.3030818

    Article  Google Scholar 

  37. Joseph Auxilius Jude, M., Diniesh, V. C., & Shivaranjani, M. (2020). Throughput stability and flow fairness enhancement of TCP traffic in multi-hop wireless networks. Wireless Networks, 26(6), 4689–4704. https://doi.org/10.1007/s11276-020-02357-5

    Article  Google Scholar 

  38. Ceballos, H. Z., Amaris, J. E. P., Jiménez, H. J., Rincón, D. A. R., Rojas, O. A., & Triviño, J. E. O. (2021). Network Simulating Using ns-3. Wireless Network Simulation (pp. 65–95). Apress. https://doi.org/10.1007/978-1-4842-6849-0_4

    Chapter  Google Scholar 

  39. NS-3 Network Simulator. (2022). Discrete-event network simulator. Retrieved Sept 12, 2022, from https://www.nsnam.org/

  40. Tian, J., & Meng, F. (2020) Comparison survey of mobility models in vehicular ad-hoc network (vanet). In 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) 337–342. IEEE. https://doi.org/10.1109/AUTEEE50969.2020.9315583

  41. VanetMobiSim. (2022). Vehicular Ad hoc Network mobility extension to the CanuMobiSim framework. Retrieved Sept 12, 2022, from http://vanet.eurecom.fr/

  42. Drago, M., Zugno, T., Polese, M., Giordani, M., & Zorzi, M. (2020) MilliCar: An ns-3 module for mmWave NR V2X networks. In Proceedings of the 2020 Workshop on ns-3, (pp. 9–16). https://doi.org/10.1145/3389400.3389402

  43. Šljivo, A., Kerkhove, D., Moerman, I., De Poorter, E., & Hoebeke, J. (2018) Interactive web visualizer for IEEE 802.11 ah ns-3 module. In Proceedings of the 10th Workshop on Ns-3 (pp.23–29). https://doi.org/10.1145/3199902.3199904

  44. NS-3 PyViz. (2022). Live simulation visualizer. Retrieved Sept 12, 2022, https://www.nsnam.org/wiki/PyViz

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Acknowledgements

To the Self Organised Networking Group (SONG) research members, who had spent more than 250 person-hours to perform vehicular simulation at Vinton Network Lab, ECE department, Kongu Engineering College.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Vinton Network Lab, Self Organised Networking Group (SONG), Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Tamil Nadu, India

    M. Joseph Auxilius Jude & V. C. Diniesh

  2. Department of Electronics and Communication Engineering, Velalar College of Engineering and Technology (Autonomous), Thindal, Erode, Tamil Nadu, India

    M. Shivaranjani

  3. Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India

    Suresh Muthusamy

  4. Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India

    Hitesh Panchal

  5. Department of Information Technology, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India

    Suma Christal Mary Sundararajan

  6. Centre for Advanced Materials, Qatar University, Doha, Qatar

    Kishor Kumar Sadasivuni

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  1. M. Joseph Auxilius Jude
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Jude, M.J.A., Diniesh, V.C., Shivaranjani, M. et al. On Minimizing TCP Traffic Congestion in Vehicular Internet of Things (VIoT). Wireless Pers Commun 128, 1873–1893 (2023). https://doi.org/10.1007/s11277-022-10024-5

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