Dynamic Resource Management for Intelligent Transportation System Applications
Primary supervisor
Additional supervisors
- Rizos Sakellariou
Additional information
- B. Zhang, X. Mao, J. Yu, and Z. Han, "Resource allocation for 5g heterogeneous cloud radio access networks with d2d communication: A matching and coalition approach," IEEE Transactions on Vehicular Technology, vol. 67, pp. 5883-5894, July 2018.
- W. Li, J. Wang, G. Yang, Y. Zuo, Q. Shao, and S. Li, "Energy efficiency maximization oriented resource allocation in 5g ultra-dense network: Centralized and distributed algorithms," Computer Communications, vol. 130, pp. 10-19, 2018.
- L. Huo and D. Jiang, "Stackelberg game-based energy-efficient resource allocation for 5g cellular networks," Telecommunication Systems, pp. 1-12, 2019.
- Y. Hao, D. Tian, G. Fortino, J. Zhang, and I. Humar, "Network slicing technology in a 5g wearable network," IEEE Communications Standards Magazine, vol. 2, no. 1, pp. 66-71, 2018.
- Y. Hao, Y. Jiang, M. S. Hossain, A. Ghoneim, J. Yang, and I. Humar, "Data-driven resource management in a 5g wearable network using network slicing technology," IEEE Sensors Journal, 2018.
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Other projects with the same supervisor
- Finding a way through the Fog from the Edge to the Cloud
- Managing the data deluge for Big Data, Internet-of-Things and/or Industry 4.0 environments
- Job and Task Scheduling and Resource Allocation on Parallel/Distributed systems including Cloud, Edge, Fog Computing
- Scheduling, Resource Management and Decision Making for Cloud / Fog / Edge Computing
- Problems in large graphs representing social networks
- Specifying and Optimising Data Wrangling Tasks
Funding
- Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.
Project description
Providing traffic information services to road users in an efficient manner has become paramount as we move to a greener economy that requires city pollution levels to be decreased. At the same time, the envisaged (post-Covid) way of life is predicted to become increasingly dependent not only on efficient transportation of physical goods, but also on efficient provision of services based on the availability of data/information collected/delivered anywhere anytime via mobile devices. To realise these objectives, numerous challenges need to be overcome, being fast and real-time data processing one of the major problems to be faced when meeting the service requirements of Intelligent Transportation System (ITS) applications, since, in these applications, the data to be processed comes from a variety of fixed and moving devices such as cars, unmanned aerial vehicles and sensors. To address this challenge, this project seeks to assess the suitability of existing cloud resource management strategies for fulfilling the requirements of ITS services and propose improved strategies for managing storage and computing resources for ITS data processing, taking into account quality-of-service requirements. In other words, the project focuses on the management of the resources involved in data processing and information provision of ITS services, which requires efficiency and dynamicity, considering that these services are underpinned by a mobility driven infrastructure shared by numerous and varied devices.