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Packet classification based aerial intelligent relay-road side unit (air-rsu) framework for vehicular ad-hoc networks

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Abstract

In this paper, we propose a packet classification based Aerial Intelligent Relay-Road Side Unit (AIR-RSU) framework introduced in every Relay-Road Side Unit (RRSU) nodes deployed across the transportation environment. The packet classification based AIR-RSU framework consists of two subsystems namely, the connectivity subsystem and packet classifier subsystem. The connectivity subsystem determines the status of network connectivity in various scenarios between RRSU nodes and vehicular nodes for every time instant Ti. In contrast, the packet classifier subsystem is used to differentiate the type of data packet requests that are invoked to access road-assisted service messages of more importance to vehicular nodes and other RRSU nodes in the transportation environment. Results indicate that both the existing AIR-RSU framework and packet classification based AIR-RSU framework provide an average of 70% dynamic network connectivity with a different combination of medium and high network connections. However, comparing to the existing AIR-RSU framework, the packet classification based AIR-RSU framework employs more amount of packet transmission with RRSU nodes due to its locality in the transportation environment.

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References

  1. Hu Z, Wen Y, Zhao H (2013) Message buffer evaluation for wireless sensor networks. In: 2013 2nd international symposium on instrumentation and measurement, sensor network and automation (IMSNA), Toronto, ON, Canada, 2013, pp 792–795. https://doi.org/10.1109/IMSNA.2013.6743396

  2. Wu J (2008) ‘Connectivity analysis of a Mobile vehicular ad hoc network with dynamic node population’, 2008 IEEE Globecom workshops. New Orleans, LA, pp 1–8. https://doi.org/10.1109/GLOCOMW.2008.ECP.60

    Book  Google Scholar 

  3. Derder A, Moussaoui A, Doukha Z, Boualouache A (2018) An online target tracking protocol for vehicular ad hoc networks. Peer-to-Peer Netw Appl 12:969–988. https://doi.org/10.1007/s12083-018-0706-5

    Article  Google Scholar 

  4. Qazi S, Alvi A, Qureshi AM, Khawaja BA, Mustaqim M (2015), ‘An Architecture for Real Time Monitoring Aerial Adhoc Network’, 2015 13th International Conference on Frontiers of Information Technology, IEEE Computer Society, pp. 154–159. https://doi.org/10.1109/FIT.2015.36

  5. Xu J, Liu W, Lang F, Zhang Y, Wang C (2010) Distance measurement model based on RSSI in WSN. Wirel Sens Netw 2:606–611. https://doi.org/10.4236/wsn.2010.28072

    Article  Google Scholar 

  6. Airborne Wireless Network, Network Information. Available from: http://www.airbornewirelessnetwork.com/index.asp

  7. Marinho MAM, Pignaton de Freitas E, Lustosa da Costa JPC, Almeida ALF, Timoteo de Sousa Junior R (2013) ‘Using Cooperative MIMO Techniques and UAV Relay Networks to Support Connectivity in Sparse Wireless Sensor Networks’, 2013 International Conference on Computing, Management and Telecommunications (ComManTel), Ho Chi Minh City, pp. 49–54. https://doi.org/10.1109/ComManTel.2013.6482364

  8. Jayarajan P, Maheswar R, Sivasankaran V, Vigneswaran D, Udaiyakumar R (2018) ‘Performance Analysis of Contention Based Priority Queuing Model Using N-Policy Model for Cluster Based Sensor Networks’, International Conference on Communication and Signal Processing, IEEE Advancing Technology for Humanity India, pp. 229–233

  9. Peterson LL, Davie BS (2012) ‘Computer Networks: A Systems Approach’, Elsevier. (Peterson & Davie, 2012)

  10. Targe PA, Satone MP (2016) VANET based real-time intelligent transportation system. Int J Comput Appl 145(6):34–38

    Google Scholar 

  11. Hafidi, S, Gharbi, N & Mokdad, L (2017) ‘Queuing and Service Management for Congestion Control in Wireless Sensor Networks Using Markov Chains’, 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, pp. 176–181. https://doi.org/10.1109/ISCC.2017.8024525

  12. Raj ASA, Palanichamy Y (2020) An aerial intelligent relay-road side unit (AIR-RSU) framework for modern intelligent transportation system. Peer-to-Peer Netw Appl 13:965–986. https://doi.org/10.1007/s12083-019-00860-x

    Article  Google Scholar 

  13. Zheng Q, Zheng K, Sun L, Leung VCM (2015) Dynamic performance analysis of uplink transmission in cluster-based heterogeneous vehicular networks. IEEE Trans Veh Technol 64(12):5584–5595. https://doi.org/10.1109/TVT.2015.2487682

    Article  Google Scholar 

  14. Zaghal R, Thabatah K, Salah S (2017) ‘Towards a smart intersection using traffic load balancing algorithm’, computing conference 2017. UK, London, pp 485–491

    Google Scholar 

  15. Chen, C, Du, X, Pei, Q & Jin, Y 2013, ‘Connectivity analysis for free-flow traffic in VANETs: a statistical approach’, International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, pp. 1–15. https://doi.org/10.1155/2013/598946

  16. Harigovindan VP, Babu AV, Jacob L (2016) Improving aggregate utility in IEEE 802.11p based vehicle-to-infrastructure networks. Telecommun Syst vol 61:359–385. https://doi.org/10.1007/s11235-015-0035-4

    Article  Google Scholar 

  17. Lall, S, Alfa, AS & Maharaj, BT (2016) The role of queueing theory in the design and analysis of wireless sensor networks: an insight. In: 2016 IEEE 14th international conference on industrial informatics (INDIN), Poitiers, France, pp 1191–1194. https://doi.org/10.1109/INDIN.2016.7819347

  18. Ilarri S, Delot T, Trillo-Lado R (2015) A data management perspective on vehicular networks. IEEE Commun Surv Tutorials 17(4):2420–2460

    Article  Google Scholar 

  19. Jing, F, Fei, G, WanSheng, W & Jianbin, X (2011) ‘A Queuing based Listening and Sleeping Mechanism in Multi-hop Wireless Mesh Sensor Networks’, Ninth IEEE International Symposium on Parallel and Distributed Processing with Applications Workshops (ISPAW), IEEE Computer Society, pp. 7–11. https://doi.org/10.1109/ISPAW.2011.44

  20. Xiao-zhu L, Jue-jia Z, Chun-di MU (2006) Collective real-time QoS in wireless sensor networks. In: 2006 international conference on wireless communications. Networking and Mobile Computing, Wuhan, pp 1–4. https://doi.org/10.1109/WiCOM.2006.264

  21. Adarsh, D, Srikanth, PC & Sathyanarayana, SM (2016) ‘GPS Free Localization based on Relative Velocity in MANETs’, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016, IEEE, pp. 3892–3895

  22. Ahmad T, Li XJ, Seet BC (2017) Parametric loop division for 3D localization in wireless sensor networks. Sensors 17(7):1–32

    Article  Google Scholar 

  23. Al-Mayouf YRB, Ismail M, Abdullah NF, Wahab AWA, Mahdi OA, Khan S, Choo KKR (2016) Efficient and stable routing algorithm based on user mobility and node density in urban vehicular network. PLoS One 11:1–24

    Article  Google Scholar 

  24. Sabir E, Kobbane A, Koulali MA, Erradi M (2013) ‘Design of an Annular Ring Ferry-Assisted Topology for Wireless Sensor Networks’, 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), Dubai, pp. 1–5. https://doi.org/10.1109/WMNC.2013.6548984

  25. Ghosh S, Unnikrishnan S (2017) ‘Reduced Power Consumption in Wireless Sensor Networks using Queue Based Approach’, 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, pp. 1–5. https://doi.org/10.1109/ICAC3.2017.8318794

  26. Le NT, Choi SW, Jang YM (2010) ‘Approximate Queuing Analysis for IEEE 802.15.4 Sensor Network’, 2010 Second International Conference on Ubiquitous and Future Networks (ICUFN), Jeju, pp. 193–198. https://doi.org/10.1109/ICUFN.2010.5547238

  27. Raut CM, Devane SR (2017) ‘Intelligent Transportation System for Smart city using VANET’, International Conference on Communication and Signal Processing. IEEE, pp. 1602–1605

  28. Ukkusuri S, Du L (2008) ‘Geometric connectivity of vehicular ad hoc networks: analytical characterization’, Transportation Research Part C, Elsevier, vol. 16, pp. 615–634. DOI: https://doi.org/10.1016/j.trc.2007.12.002

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Acknowledgments

This research work is supported by the Visvesvaraya Ph.D. Scheme for Electronics and Information Technology, New Delhi. One of the authors, Mr. Samson Arun Raj A, is thankful to the Visvesvaraya Ph.D. Scheme for Electronics and Information Technology for providing financial support to carry out this research work.

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Correspondence to A. Samson Arun Raj.

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Raj, A.S.A., Palanichamy, Y. Packet classification based aerial intelligent relay-road side unit (air-rsu) framework for vehicular ad-hoc networks. Peer-to-Peer Netw. Appl. 14, 1132–1153 (2021). https://doi.org/10.1007/s12083-021-01092-8

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