nach oben

Erschienen in:

19.10.2022

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

verfasst von: M. Joseph Auxilius Jude, V. C. Diniesh, M. Shivaranjani, Suresh Muthusamy, Hitesh Panchal, Suma Christal Mary Sundararajan, Kishor Kumar Sadasivuni

Erschienen in: Wireless Personal Communications | Ausgabe 3/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Optimized DPMZM Based RoF Link Against Fiber Impairments

Nächster Artikel N-Dimensional Markov Chain Analysis of the Starvation Issue for IEEE 802.15.6 Slotted Aloha Algorithm

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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_48CrossRef

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

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

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/10.1080/15567036.2022.2067268CrossRef

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.2019.1800339CrossRef

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/jsan9020022CrossRef

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.2020.107868CrossRef

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.

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.2019.111136CrossRef

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.2020.2965929CrossRef

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/s11235-020-00704-7CrossRef

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/s21041121CrossRef

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.2020.3027074CrossRef

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.2021.1003862CrossRef

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.2020.3016634CrossRef

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.5856CrossRef

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.2017.01.001CrossRef

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.2017.2759300CrossRef

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.2018.06.001CrossRef

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/s11277-018-5675-8CrossRef

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/s11276-020-02459-0CrossRef

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.2016.11.002CrossRef

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.2016.03.020CrossRef

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/electronics11091479CrossRef

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-xCrossRef

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/10.1109/49.464716CrossRef

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.2020.107690CrossRef

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.2015.03.048CrossRef

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-yCrossRef

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.2017.084570CrossRef

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_13CrossRef

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.2020.06.002CrossRef

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.2020.3030818CrossRef

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/s11276-020-02357-5CrossRef

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_4CrossRef

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

Titel: On Minimizing TCP Traffic Congestion in Vehicular Internet of Things (VIoT)
verfasst von: M. Joseph Auxilius Jude
V. C. Diniesh
M. Shivaranjani
Suresh Muthusamy
Hitesh Panchal
Suma Christal Mary Sundararajan
Kishor Kumar Sadasivuni
Publikationsdatum: 19.10.2022
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 3/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-10024-5

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Zukunftswerkstatt Sales Excellence_ieS/© Springer Fachmedien Wiesbaden GmbH, Search Icon, Banner Hanser, Bunte Männchen, die Kunden darstelle, werden von einem riesigen Magneten angezogen. /© Oleksiy Mark, Dr. Daniel Schneider/© Fraunhofer IESE, Interview Level Ten PPA Bild/© LevelTen, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, Zukunftswerkstatt Sales Excellence 2024/© AndreyPopov / Getty Images / iStock, 2023_Antrieb/© supervisuell, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2023

Power System Reconfiguration in Distribution System for Loss Minimization Using Optimization Techniques: A Review

Design of a Compact Double-Square-Ring-Shaped Dual-Band Antenna for WiMAX/WLAN Applications

Object Detection for Night Surveillance Using Ssan Dataset Based Modified Yolo Algorithm in Wireless Communication

Evaluation of Physical Layer Security for UAV-Enabled Wireless Networks over Fading Channels

Interpretation and Investigations of Topology Based Routing Protocols Applied in Dynamic System of VANET

N-Dimensional Markov Chain Analysis of the Starvation Issue for IEEE 802.15.6 Slotted Aloha Algorithm

Neuer Inhalt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.