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Detection and Recognition of Moving Biological Objects for Autonomous Vehicles Using Intelligent Edge Computing/LoRaWAN Mesh System

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2020, ruSMART 2020)

Abstract

Currently, 5G/IMT-2020 networks with their possibilities become more and more services of new areas. These services are integrated into different human life activities. And in several cases, human life depends on Artificial Intelligence technologies, Autonomous Systems, and the Internet of Things (IoT), etc. Autonomous vehicles provide very strict requirements to the network in terms of ultra-low latency, high throughput, and wide coverage. To support these requirements, additional technologies must be employed. The current paper discusses the possibility of the use of airborne platforms aiming to support the terrestrial networks for autonomous vehicles realization as a part of delay-critical applications. Airborne platforms will help in the provisioning of safe road trips by delivering time-critical information to the vehicles globally, even in remote areas. In this paper, we discuss requirements and potential solutions for supporting the autonomous vehicle infrastructure, as a part of an intelligent transportation system. It’s proposed to use a sensor network along the road, consists of energy-efficient sensors that can connect in a Mesh network. Also, a novel approach for the detection of biological objects activity on the roadside, based on Artificial Intelligence technologies are suggested.

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References

  1. Galinina, O., Andreev, S., Gerasimenko, M., Koucheryavy, Y., Himayat, N., Yeh, S.-P., Talwar, S.: Capturing spatial randomness of heterogeneous cellular/WLAN deployments with dynamic traffic. IEEE J. Sel. Areas Commun. 32(6), 1083–1099 (2014). art. no. 6824742

    Article  Google Scholar 

  2. Ateya, A.A., Muthanna, A., Koucheryavy, A.: 5G framework based on multi-level edge computing with D2D enabled communication. In: International Conference on Advanced Communication Technology, ICACT, vol. 2018-February, pp. 507–512. Institute of Electrical and Electronics Engineers Inc. (2018) https://doi.org/10.23919/ICACT.2018.832381

  3. Ometov, A., et al.: Toward trusted, social-aware D2D connectivity: bridging across the technology and sociality realms. IEEE Wirel. Commun. 23(4), 103–111 (2016). art. no. 7553033

    Article  Google Scholar 

  4. Volkov, A., Ateya, A.A., Muthanna, A., Koucheryavy, A.: Novel AI-based scheme for traffic detection and recognition in 5G based networks. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART -2019. LNCS, vol. 11660, pp. 243–255. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30859-9_21

    Chapter  Google Scholar 

  5. Muthanna, A., Volkov, A., Khakimov, A., Muhizi, S., Kirichek, R., Koucheryavy, A.: Framework of QoS management for time constraint services with requested network parameters based on SDN/NFV infrastructure. In: International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, vol. 2018-November). IEEE Computer Society (2019) https://doi.org/10.1109/ICUMT.2018.8631274

  6. Petrov, V., Samuylov, A., Begishev, V., Moltchanov, D., Andreev, S., Samouylov, K., Koucheryavy, Y.: Vehicle-based relay assistance for opportunistic crowdsensing over narrowband IoT (NB-IoT). IEEE Internet Things J. 5(5), 3710–3723 (2018). art. no. 7857676

    Article  Google Scholar 

  7. Volkov, A., Khakimov, A., Muthanna, A., Kirichek, R., Vladyko, A., Koucheryavy, A.: (2017) Interaction of the IoT traffic generated by a Smart city segment with SDN core network. In: WWIC 2017 International Conference on Wired/Wireless Internet Communication, pp. 115–126, 0302-9743 eISSN: 1611-3349. Springer-Verlag GmbH, Heidelberg (2017)

    Google Scholar 

  8. Volkov, A., Proshutinskiy, K., Adam, A.B.M., Ateya, A.A., Muthanna, A., Koucheryavy, A.: SDN load prediction algorithm based on artificial intelligence. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds.) DCCN 2019. CCIS, vol. 1141, pp. 27–40. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36625-4_3

    Chapter  Google Scholar 

  9. Tran, T.X., Hajisami, A., Pandey, P., Pompili, D.: Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Commun. Mag. 55, 54–61 (2017)

    Article  Google Scholar 

  10. Gerla, M., Lee, E.K., Lee, U.: Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds. In: 2014 IEEE World Forum on Internet of Things, WF-IoT 2014, pp. 241–246. IEEE Computer Society (2014) https://doi.org/10.1109/wf-iot.2014.6803166

  11. Bonnefon, J.F., Shariff, A., Rahwan, I.: The social dilemma of autonomous vehicles. Science 352, 1573–1576 (2016). https://doi.org/10.1126/science.aaf2654

    Article  Google Scholar 

  12. Ometov, A., et al.: Feasibility characterization of cryptographic primitives for constrained (wearable) IoT devices. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops, art. no. 7457161, (2016)

    Google Scholar 

  13. Rathore, H., Agarwal, S., Sahay, S. K., Sewak, M.: Malware detection using machine learning and deep learning. In: International Conference on Big Data Analytics, pp. 402–411. Springer, Cham (2018)

    Google Scholar 

  14. Sewak, M., Sahay, S.K., Rathore, H.: An investigation of a deep learning based malware detection system. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, p. 26. ACM (2018)

    Google Scholar 

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Acknowledgment

The publication has been prepared with the support of the “RUDN University Program 5-100” (recipient Ammar Muthanna).

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Correspondence to Ammar Muthanna .

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Artem, V., Al-Sveiti, M., Elgendy, I.A., Kovtunenko, A.S., Muthanna, A. (2020). Detection and Recognition of Moving Biological Objects for Autonomous Vehicles Using Intelligent Edge Computing/LoRaWAN Mesh System. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12526. Springer, Cham. https://doi.org/10.1007/978-3-030-65729-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-65729-1_1

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