| EPSRC Reference: |
EP/Z53433X/1 |
| Title: |
EIMS: Edge Intelligence Empowered Vehicular Multimedia Service Optimization |
| Principal Investigator: |
Min, Professor G |
| Other Investigators: |
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| Department: |
Computer Science |
| Organisation: |
University of Exeter |
| Scheme: |
UKRI Postdoc Guarantee TFS |
| Starts: |
01 January 2025 |
Ends: |
31 December 2026 |
Value (£): |
192,297
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Summary on Grant Application Form |
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Real-time multimedia service optimization in the Internet-of-Vehicles (IoV) has attracted significant interest from both academia and industry due to the constantly increasing demands on vehicular multimedia applications such as real-time navigation, high-definition maps, and 360-degree video for auxiliary driving. The future IoV is envisioned to incorporate caching, computation, and communications (3C) resources to handle real-time multimedia transmission effectively and to create a safe, effective, and comfortable driving environment. However, the prediction, caching and delivery of streaming media contents confront significant obstacles, given the high mobility of vehicles, intermittent information transmission, high dynamics of content requests, and complexity of working scenarios. To address these challenges, this project aims to seamlessly synergize the benefits of vehicular communication protocols and Machine Learning technology in order to maximize the 3C resource utilization, enhance the Quality-of-Service, and protect user privacy. First, a robust 3C resource integration mechanism empowered by edge intelligence with a unified assessment model will be proposed to optimize real-time vehicular multimedia services. Second, a privacy-preserving federated multi-modal learning approach will be developed to analyze the spatial-temporal connection between vehicle trajectory and service requests and create a reliable traffic prediction model. Third, a hierarchical Deep Reinforcement Learning based decision-making method will be developed to construct an adaptive multimedia transmission control model that can simultaneously optimize the 3C resource allocation as well as the selection of transmission bitrate and reconstruction resolution. The outcomes of this project will produce cutting-edge theoretical and technical advancements that will aid in technical advancement, standardization, and market application of automakers and mobile communication service providers.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ex.ac.uk |