Cloud and IoT-Based Vehicular Ad Hoc Networks. Группа авторов
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23. Alnwaimi, G., Vahid, S., & Moessner, K., Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments. IEEE Transactions on Wireless Communications, 14, 4, 2294–2308, 2014.
24. Wang, L. C., & Cheng, S. H. Data-driven resource management for ultra-dense small cells: An affinity propagation clustering approach. IEEE Transactions on Network Science and Engineering, 6, 3, 267–279, 2018.
25. Parwez, M. S., Rawat, D. B., & Garuba, M., Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network. IEEE Transactions on Industrial Informatics, 13, 4, 2058–2065, 2017.
26. Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H., Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless communications, 24, 5, 175–183, 2017.
27. Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K. C., & Hanzo, L., Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24, 2, 98–105, 2016.
28. Han, S. Y., Abu-Ghazaleh, N. B., & Lee, D. Efficient and consistent path loss model for mobile network simulation. IEEE/ACM Transactions on Networking, 24, 3, 1774–1786, 2015.
29. Liu, J., Deng, R., Zhou, S., & Niu, Z., Seeing the unobservable: Channel learning for wireless communication networks. In 2015 IEEE Global Communications Conference (GLOBECOM), (pp. 1–6), IEEE, 2015.
30. Minoli, D., & Occhiogrosso, B., Practical aspects for the integration of 5G networks and IoT applications in smart cities environments. Wireless Communications and Mobile Computing, 2019.
31. Osseiran, A., Braun, V., Hidekazu, T., Marsch, P., Schotten, H., Tullberg, H., ... & Schellman, M., The foundation of the mobile and wireless communications system for 2020 and beyond: Challenges, enablers and technology solutions. In 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), (pp. 1–5), IEEE, 2013.
32. Popovsk, P., Brau, V., Mayer, H. P., Fertl, P., Ren, Z., Gonzales-Serrano, D., ... & Chatzikokolakis, K., EU FP7 INFSO-ICT-317669 METIS, D1. 1 Scenarios, requirements and KPIs for 5G mobile and wireless system, 2013.
33. Stefanovic, C., & Popovski, P., ALOHA random access that operates as a rate-less code. IEEE Transactions on Communications, 61, 11, 4653–4662, 2013.
34. Zheng, Z., Cai, L. X., & Shen, X., Sustainable wireless networks. Springer Science & Business Media, 2013.
35. Basheer, M. R., & Jagannathan, S. Localization and tracking of objects using cross-correlation of shadow fading noise. IEEE Transactions on Mobile Computing, 13, 10, 2293–2305, 2013.
36. Akdeniz, M. R., Liu, Y., Rangan, S., & Erkip, E., Millimeter wave picocellular system evaluation for urban deployments. In 2013 IEEE Globecom Workshops (GC Wkshps), pp. 105–110, IEEE, 2013.
37. Bai, T., & Heath, R. W., Coverage analysis for millimeter wave cellular networks with blockage effects. In 2013 IEEE Global Conference on Signal and Information Processing, 727–730, IEEE, 2013.
38. Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., ... & Gutierrez, F. Millimeter wave mobile communications for 5G cellular: It will work!. IEEE access, 1, 335–349, 2013.
39. Vermesan, O., & Friess, P. (Eds.), Internet of things: converging technologies for smart environments and integrated ecosystems. River publishers, 2013.
40. Namboodiri, V., and Gao, L., Energy- aware tag anti-collision protocols for RFID systems, IEEE Transactions on Mobile Computing, 9, 1, 44–59, January 2010.
41. Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L., A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci., 79, 8, 1230–1242, 2013.
42. https://www.finoit.com, Finoit Technologies, Finoit Technologies, https://www.finoit.com/blog/top-15-sensor-types-used-iot, 2017, August 23.
43. Hossain, S., 5G wireless communication systems. American Journal of Engineering Research (AJER), 2, 10, 344–353, 2013.
44. Kumar, A., Payal, M., Dixit, P., Chatterjee, J.M., Framework for Realization of Green Smart Cities Through the Internet of Things (IoT), in: Trends in Cloud-based IoT, F. Al-Turjman (Ed.), EAI/Springer Innovations in Communication and Computing. Springer, Cham, 2020.
45. Mathur, S. and Arora, A., Internet of Things (IoT) and PKI-Based Security Architecture, in: Industrial Internet of Things and Cyber-Physical Systems: Transforming the Conventional to Digital, pp. 25–46, IGI Global, India, 2020.
* Corresponding author: [email protected]; [email protected]
2
Internet of Things-Based Service Discovery for the 5G-VANET Milieu
P. Dharanyadevi1*, M. Julie Therese2 and K. Venkatalakshmi3
1Department of Computer Science, Pondicherry University, Puducherry, India
2Department of Electronics and Communication Engineering, Sri ManakulaVinayagar Engineering College, Puducherry, India
3Electronics and Communication Engineering, Anna University Tindivanam Campus, Tindivanam, India
Abstract
The advancement in the internet of things based vehicular networks elevates the newfangled demands in the proficient discovery process. The major concern in vehicular milieu is to provide seamless connectivity with ultra-fast services to the users. To address this concern, Vehicular Ad Hoc NETwork (VANET) milieu is integrated with 5G. The service discovery is the process that retrieves the best resources (service) as per the vehicular consumer needs. The vital issue in the 5G-VANET about of an application-oriented technology is to discern the services accurately and efficiently as per the user’s needs with high reliability, low latency, and high-bandwidth. The first part of the chapter deals with the fundamentals and technological details of VANET, 5G, the need of integrating the VANET with 5G, and the need for service of discovery and also discusses the service discovery mechanism. The second part of the chapter discusses the service discovery methods and frameworks and also discusses the service discovery architecture in the 5G-VANET milieu. The third part of the chapter discusses the petty performance evaluation metrics, service discovery in the 5G-VANET milieu advantage and disadvantage. The final part of this chapter discusses the future directions of service discovery in the 5G-VANET milieu.
Keywords: VANET, 5G, Internet of Things, service discovery, performance metrics
2.1 VANET
VANET milieu recedes into the context of the Internet of Things, which provides consumers with relevant information and services at any moment, everywhere [1, 2]. Without sufficient understanding of the discovery mechanism, retrieving the efficient service is a big task. VANET is distinguished into infrastructure (IF) and infrastructure-less (IFL) networks. The IF network is dependent on the fixed elements. The IFL (Ad Hoc) networks are a lightweight network with no fixed components, where the mobile node is capable of interacting with each other within its coverage area [3].
As shown in Figure 2.1, in VANET each node (vehicle) is proficient in communicating with other nodes and access points within its range [3]. The most important VANET communication