Abstract
This paper presents a revolutionary research initiative focused on improving municipal fleet management in smart cities by leveraging the power of 5G technology. Through adopting 5G, the municipality gains access to real-time communication, precise vehicle tracking, optimized route planning, and improved operational efficiency. Delving into the realm of 5G technology, the study examines its multiple applications in fleet management, including real-time monitoring and predictive maintenance strategies. The initial findings establish a robust foundation, laying the groundwork for forthcoming initiatives aimed at enhancing municipal fleet operations in smart cities through the seamless integration of 5G connectivity. Our objective is to amplify the impact of our work, making a substantial contribution to the continuous evolution of smart city infrastructures. Subsequent research endeavors will delve even deeper into the expansive potential of 5G, exploring its applications in fleet optimization, intelligent transportation systems, and innovative solutions for sustainable mobility.
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References
5G in release 17-strong radio evolution. Technical report, 3GPP, TS 23.501 V16.1.0 (2019)
System architecture for the 5G system. Technical report, 3GPP, TS 23.501 V16.1.0 (2019)
Minimum requirements related to technical performance for IMT-2020 radio interface (s). Technical report, document ITU-R SG05 (2017). [Online]
3GPP: Study on scenarios and requirements for next generation access technologies (release 16). Technical report, TR 38.913 V16.0.0 (2020)
Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Serv. Appl. 6(1), 1–15 (2015)
Alsaleh, A.: The impact of various communication methods: how vehicle-to-vehicle and vehicle-to-infrastructure messages affect the performance of driving smart vehicles. Available at SSRN 4339865
Bertenyi, B.: 5G evolution: what’s next? IEEE Wirel. Commun. 28(1), 4–8 (2021)
Bibri, S.E.: On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review. J. Big Data 6(1), 1–64 (2019)
Bibri, S.E., Krogstie, J.: Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustain. Urban Areas 31, 183–212 (2017)
Biswas, A., Wang, H.C.: Autonomous vehicles enabled by the integration of IoT, edge intelligence, 5G, and blockchain. Sensors 23(4), 1963 (2023)
Cerasi, I.R.: The potential of autonomous and connected sweepers for smart and sustainable cities. Int. J. Transp. Dev. Integration 6(1), 37–57 (2022)
Coors, V., Pietruschka, D., Zeitler, B.: iCity. Transformative Research for the Livable, Intelligent, and Sustainable City: Research Findings of University of Applied Sciences Stuttgart. Springer Nature (2022)
CSS Electronic: CAN Bus Data Loggers (2023). https://www.csselectronics.com/
Dabeedooal, Y.J., Dindoyal, V., Allam, Z., Jones, D.S.: Smart tourism as a pillar for sustainable urban development: an alternate smart city strategy from Mauritius. Smart Cities 2(2), 153–162 (2019)
Dai, C., Liu, X., Lai, J., Li, P., Chao, H.C.: Human behavior deep recognition architecture for smart city applications in the 5G environment. IEEE Netw. 33(5), 206–211 (2019)
Ghosh, A., Maeder, A., Baker, M., Chandramouli, D.: 5G evolution: a view on 5G cellular technology beyond 3G pp release 15. IEEE Access 7, 127639–127651 (2019)
Gohar, A., Nencioni, G.: The role of 5G technologies in a smart city: the case for intelligent transportation system. Sustainability 13(9), 5188 (2021)
Ji, H., Huang, H.: The integration and development trend of China’s 5G technology and smart cleaning. In: Journal of Physics: Conference Series, vol. 1812, p. 012015. IOP Publishing (2021)
Joseph, P.C., Kumar, S.P.: Design and development of OBD-II compliant driver information system. Indian J. Sci. Technol. 8(21) (2015)
Kombate, D., et al.: The internet of vehicles based on 5G communications. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 445–448. IEEE (2016)
Li, W., Bhushan, B., Gao, J., Zhang, P.: Smartclean: smart city street cleanliness system using multi-level assessment model. Int. J. Softw. Eng. Knowl. Eng. 28(11n12), 1755–1774 (2018)
Malasinghe, L.P., Ramzan, N., Dahal, K.: Remote patient monitoring: a comprehensive study. J. Ambient. Intell. Humaniz. Comput. 10, 57–76 (2019)
Nagaraj, P., Lakshmanaprakash, S., Muneeswaran, V.: Edge computing and deep learning based urban street cleanliness assessment system. In: 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), vol. 01, pp. 1–6 (2022)
Ramai, C., Ramnarine, V., Ramharack, S., Bahadoorsingh, S., Sharma, C.: Framework for building low-cost OBD-II data-logging systems for battery electric vehicles. Vehicles 4(4), 1209–1222 (2022)
Rao, S.K., Prasad, R.: Impact of 5G technologies on smart city implementation. Wirel. Pers. Commun. 100, 161–176 (2018)
Rathore, M.M., Paul, A., Hong, W.H., Seo, H., Awan, I., Saeed, S.: Exploiting IoT and big data analytics: defining smart digital city using real-time urban data. Sustain. Urban Areas 40, 600–610 (2018)
Tesla: Tesla (2023). https://www.tesla.com/
Tesla, Inc.: Fleet telemetry (2023). https://github.com/teslamotors/fleet-telemetry
Traboulsi, S.: Overview of 5G-oriented positioning technology in smart cities. Procedia Comput. Sci. 201, 368–374 (2022)
Traboulsi, S., Uckelmann, D.: 5G as enabler technology for smart city use cases. In: 2022 9th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 1–7. IEEE (2022)
TU: Work plan, timeline, process and deliverables for the future development of IMT. ITU-R WP5D (2015)
Usman, M.A., Philip, N.Y., Politis, C.: 5G enabled mobile healthcare for ambulances. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE (2019)
Wang, L., Wang, L., Liu, W., Zhang, Y.: Research on fault diagnosis system of electric vehicle power battery based on OBD technology. In: 2017 International Conference on Circuits, Devices and Systems (ICCDS), pp. 95–99. IEEE (2017)
Zhang, P., Zhao, Q., Gao, J., Li, W., Lu, J.: Urban street cleanliness assessment using mobile edge computing and deep learning. IEEE Access 7, 63550–63563 (2019)
Acknowledgment
This work is financially supported by the Federal Ministry of Education and Research (BMBF), Germany, under the funding code 13FH9I02IA, as part of the FH-Impuls 2016 research project: ’Urban Digital Twins for the Intelligent City.’ However, the primary responsibility for the content of this publication rests solely with the first author.
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Traboulsi, S., Uckelmann, D. (2024). Enhancing Municipal Fleet Management in Smart Cities Through 5G Integration. In: Auer, M.E., Langmann, R., May, D., Roos, K. (eds) Smart Technologies for a Sustainable Future. STE 2024. Lecture Notes in Networks and Systems, vol 1028. Springer, Cham. https://doi.org/10.1007/978-3-031-61905-2_7
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DOI: https://doi.org/10.1007/978-3-031-61905-2_7
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