Mobile Edge Computing Final Year Projects with Source Code

Byadmin Final Year Projects

Mobile Edge Computing Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Mobile Edge Computing projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Mobile Edge Computing Final Year Projects

  1. Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Industrial IoT in MEC Federation System
    This study focuses on improving smart industrial systems that use IoT devices. These devices have limited power and memory, so the project uses mobile edge computing to help them process data more efficiently. The researchers developed a smart method using deep learning to decide how tasks are shared and resources are used across devices. Their approach reduces energy use and processing delays in real-world scenarios.
  2. A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning
    This project predicts which roadside unit a moving vehicle will connect to next. By knowing this early, the network can store the needed data in advance and reduce delay for users. The system uses learning methods that allow each roadside unit to make its own predictions. Tests in different cities show that the method predicts vehicle movement with high accuracy, even in complex traffic conditions.
  3. Challenges and Solutions for Service Continuity in Inter-PLMN Handover for Vehicular Applications
    This project focuses on improving mobile network connectivity for connected and automated vehicles. It addresses problems that occur when vehicles switch between network cells or different networks, which can cause service interruptions. The work proposes solutions like using dual modems, adaptive video streaming, and special servers to maintain reliable connections. Tests on 5G networks show these solutions help, but current networks still need improvements for fully reliable vehicle communication.
  4. Machine Learning Empowered Emerging Wireless Networks in 6G Recent Advancements Challenges and Future Trends
    This project explains how future 6G networks will use machine learning to make communication faster, smarter, and more reliable. It shows how ML helps the network manage resources on its own and solve problems like energy use, delays, and smooth connections. The paper reviews different ML methods and how they improve new types of networks, such as device-to-device and vehicular systems. It also discusses open challenges and future research directions for building intelligent 6G networks.
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Mobile Edge Computing Project Synopsis & Presentation

Final Year Projects helps prepare Mobile Edge Computing project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

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