Edge Computing Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Edge Computing ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Edge Computing Projects for MTech, MS & PhD Students

Latest Edge Computing Projects

  1. Combining Lyapunov Optimization and Deep Reinforcement Learning for D2D Assisted Heterogeneous Collaborative Edge Caching
    This project focuses on improving content sharing in wireless networks. Devices can share data directly with nearby devices or get it from a base station. The method uses smart learning to decide which devices should store and share content. It reduces delays, saves energy, and keeps the network running smoothly.
  2. Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing
    This project focuses on helping mobile devices handle heavy computing tasks more efficiently. Instead of doing all tasks on the device, some work is sent to powerful edge servers nearby. The goal is to finish tasks faster while using less battery. The project uses smart algorithms that learn over time to make the best decisions for offloading tasks.
  3. Hierarchical Reinforcement Learning for Multi-Layer Multi-Service NonTerrestrial Vehicular Edge Computing
    This project focuses on improving computing for smart vehicles. It combines ground-based and satellite edge computing to help vehicles process data faster and in more areas. The system uses machine learning to decide where and how much data to process for lower delay and energy use. Simulations show that this approach works better than existing methods.
  4. Sustainable Mobility in B5G/6G: V2X Technology Trends and Use Cases
    This project studies how mobile communication technologies can make transportation more sustainable. It looks at smart city vehicles that communicate with each other and with infrastructure. The research explores trends like climate-friendly systems, cloud and edge computing, and AI to improve vehicle networks. It also estimates how these technologies can reduce fuel use and greenhouse gas emissions.
  5. Secure and Fine-Grained Access Control With Optimized Revocation for Outsourced IoT EHRs With Adaptive Load-Sharing in Fog-Assisted Cloud Environment
    This project focuses on keeping patient health records safe when stored and shared through IoT devices and the cloud. It uses blockchain to check who can access the data and fog computing to handle heavy encryption tasks. The system allows easy removal of users and ensures fast, secure data sharing. Tests show it works efficiently while protecting sensitive information.
  6. A C-ITS Architecture for MEC and Cloud Native Back-End Services
    This project builds a smart system that helps vehicles communicate quickly and safely using cloud and edge computing. It connects cars, nearby servers, and cloud systems to share traffic and road data in real time. The design reduces delays and supports many connected vehicles at once. It was tested to ensure smooth and scalable communication between all parts.
  7. A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds
    This project develops a cloud system that works close to users, at the edge of the network. It creates virtual clouds on physical machines and shares resources like CPU, memory, and storage efficiently. Each virtual cloud stays separate, keeping data and operations isolated. Users can also switch between different virtual clouds safely, while the system manages security automatically.
  8. Blockchain-Based Decentralized Storage Design for Data Confidence Over Cloud-Native Edge Infrastructure
    This project focuses on building a new way to store data across many devices instead of in one central place. It uses cloud and blockchain ideas to make storage faster, more secure, and private. The system works well on edge devices and improves data transfer compared to existing methods. Overall, it solves common problems in decentralized data management.
  9. Cloud-native orchestration framework for network slice federation across administrative domains in 5G/6G mobile networks
    This project focuses on improving mobile networks for connected and automated vehicles. It ensures that users keep a smooth connection even when moving between different network operators. The researchers designed a system that allows mobile operators to share network resources efficiently. They tested it on a 5G platform and studied how different strategies affect performance.
  10. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid
    This project builds a smart system that quickly finds faults in the power grid. It uses both cloud and edge computers to share the work of detecting problems. The edge devices do fast, light processing, while the cloud handles heavy tasks. This setup reduces delay and makes the power grid run more efficiently in real time.
  11. Design of an Efficient and Secure Authentication Scheme for Cloud-FogDevice Framework Using Key Agreement and Management
    This project improves the security of communication between smart devices, fog nodes, and cloud servers. It introduces a new method to verify and protect all connected systems from attacks. The proposed approach keeps user data private and ensures secure information exchange. It is tested and proven to be both safe and efficient for real-time IoT applications.
  12. Dynamic Sizing of Cloud-Native Telco Data Centers With Digital Twin and Reinforcement Learning
    This project focuses on making telecom edge data centers more efficient. It predicts how much computing power is needed at different times of the day. Then it adjusts the number of active servers to save energy and reduce costs. The system uses smart algorithms and machine learning to make these adjustments quickly and reliably.
  13. Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems
    This project focuses on finding the best cloud settings for different applications using smart optimization techniques. It combines machine learning and Bayesian methods to predict and choose efficient configurations. The approach works for both public and private clouds and can handle limits like execution time or accuracy. Tests show it saves time and cost compared to existing methods.
  14. Optimizing Structural Health Monitoring Systems Through Integrated Fog and Cloud Computing Within IoT Framework
    This project is about creating a smart system to check the health of buildings and structures using the Internet of Things. Small sensors on the structure collect movement data and send it to a local computer. If the system detects any damage, the data is sent to the cloud for detailed analysis. The system is tested and found to be reliable, efficient, and cost-effective for detecting structural problems.
  15. Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
    This project studies how to efficiently assign computing tasks across different types of devices, from sensors to data centers and quantum computers. The researchers test quantum algorithms to solve this complex scheduling problem. They find that one algorithm, VQE, works better when carefully set up. Real quantum hardware experiments show it can handle larger problems faster than classical computers.
  16. A Framework for Cognitive, Decentralized Container Orchestration
    This project introduces CODECO, a system that helps decide the best infrastructure for running modern Internet applications on a mix of cloud and edge devices. It uses smart, decentralized methods to handle challenges like weak connections or device failures. CODECO aims to meet user goals like energy efficiency or reliability. The framework is open-source and can be used and tested by researchers.
  17. A Novel Authentication Protocol for 5G gNodeBs in Service Migration Scenarios of MEC
    This project focuses on making edge computing safer and faster. It ensures that services moving between network nodes are secure and verified. The researchers designed a new authentication method that follows current mobile network standards. They tested it using software tools and a real 5G network setup to confirm it works well.
  18. An Online Simulated Annealing-Based Task Offloading Strategy for a Mobile Edge Architecture
    This project develops SATS, a method to assign tasks quickly in mobile edge computing systems. It uses a smart trial-and-error approach to schedule tasks in real time. The system works best when it slightly overestimates the number of incoming requests. This strategy improves how many tasks are accepted and reduces the time they take to process.
  19. Artificial Intelligence-Defined Wireless Networking for Computational Offloading and Resource Allocation in Edge Computing Networks
    This project focuses on improving how data and computing resources are managed in next-generation 5G networks. It brings computing closer to mobile users so applications run faster and more reliably. The researchers developed an AI-based system that decides how to share resources and offload tasks in real time. Their approach can serve more users efficiently, even in busy and fast-changing network conditions.
  20. At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence
    This project explores how the upcoming 6G wireless networks can benefit from artificial intelligence. It explains how traditional AI methods need large amounts of data, while generative AI can work even with limited or incomplete data. The study reviews different AI models and shows how generative AI can improve network design, security, and communication performance. It also highlights future research directions and challenges in 6G networks.
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How We Help You with Edge Computing Projects

At UniPhD, we provide complete guidance and support for Edge Computing ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.

Edge Computing Thesis and Dissertation Writing

UniPhD has a team of experienced academic writers who specialize in Edge Computing research and thesis development. We offer fast-track dissertation writing services to help you complete your Edge Computing thesis or dissertation smoothly and on time.

Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.

Edge Computing Research Paper Publishing Support

UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.

Project Synopsis and Presentation Support

We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Edge Computing final year project.

Edge Computing Research Support for PhD Scholars

UniPhD offers advanced Edge Computing research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.

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