Mobile Edge Computing Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Mobile 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.
Latest Mobile Edge Computing Projects
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Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
This project focuses on making mobile networks smarter and cheaper to run. It studies how to set up and manage different parts of the network, like base stations and virtual units, depending on traffic and resources. The researchers used a type of artificial intelligence called deep reinforcement learning to find the best setup automatically. Their method reduces network costs a lot compared to older approaches. -
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. -
Fast Context Adaptation in Cost-Aware Continual Learning
This project studies how smart computer programs can manage resources in 5G networks. It looks at a problem where learning these strategies can use up the network’s resources and affect users. The researchers propose a method that lets the program learn quickly while using very few resources. Their approach adapts to changes and keeps the user experience smooth. -
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. -
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. -
ATHENA: An Intelligent Multi-x Cloud Native Network Operator
This project develops Athena, a new system for managing modern mobile networks like 4G and 5G. It makes networks more flexible, efficient, and easy to control across different vendors and devices. Athena reduces operational overhead, saves energy, and keeps the network highly reliable. The system was tested and shown to improve speed, performance, and sustainability. -
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. -
A Hyper-Heuristic Approach for Quality of Experience Aware Service Placement Scheme in 5G Mobile Edge Computing
This project focuses on improving 5G mobile edge computing. It aims to place computing services closer to users so they get faster responses. The system predicts user movement and moves services ahead of time to improve user experience. It uses smart algorithms to balance speed, cost, and efficiency in large networks. -
A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing
This project focuses on improving how mobile devices handle heavy computing tasks. It moves tasks from the device to nearby edge servers to save time and energy. The researchers used smart learning methods so the system can adapt to changing conditions. Their approach shows faster and more stable performance across different situations. -
A Novel Quantum Hash-Based Attribute-Based Encryption Approach for Secure Data Integrity and Access Control in Mobile Edge ComputingEnabled Customer Behavior Analysis
This project focuses on improving the security and reliability of mobile edge computing systems used in consumer behavior research. It introduces a new method called Quantum Hash-Based Attribute-Based Encryption (QH-ABE) to protect data and control who can access it. The system uses quantum computing ideas and a special algorithm to detect any changes in data. This approach makes data handling faster, safer, and more trustworthy in modern edge computing environments. -
A Spiking Reinforcement Trajectory Planning for UAV-Assisted MEC Systems
This project focuses on reducing energy use in drones and mobile devices that work together for edge computing. It uses a new learning method that combines brain-inspired neural networks with deep learning to plan drone movements efficiently. The proposed approach trains faster and uses fewer resources than traditional methods. It helps drones make smarter and quicker decisions while saving energy. -
An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud
This project focuses on improving how wearable medical devices send and process data. It introduces a smart method that decides when and how to send heavy computing tasks to nearby mobile servers. By using multiple servers together, it reduces delay and saves energy. This makes remote medical applications like surgery faster and more reliable. -
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. -
Attention Mechanism-Aided Deep Reinforcement Learning for Dynamic Edge Caching
This project focuses on improving mobile networks by predicting what content users will need and storing it on nearby devices before it is requested. It designs a smart system that decides what to cache and when, using a type of artificial intelligence. The goal is to reduce network traffic and make better use of limited storage and network resources. The proposed method was tested and shown to work effectively in improving speed and efficiency. -
CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System
This project improves smart transportation systems by combining cloud computing with edge devices. It uses a deep learning model called CNN, optimized with a special algorithm to make it faster and smaller. The model can quickly and accurately recognize traffic signs and objects from cameras. Tests show it works better than existing methods with very high accuracy. -
Computation Rate Maximization for Wireless-Powered Edge Computing With Multi-User Cooperation
This project studies a system where small devices can share computing tasks and get energy wirelessly. Devices work together in groups to split tasks between themselves and a central hub. The goal is to finish more computing work faster while saving energy. The team developed algorithms, including one using deep learning, to make this process efficient and quick. -
Design of Computing-Aware Traffic Steering Architecture for 5G Mobile User Plane
This project focuses on improving 5G networks to make them faster and more reliable at the edge of the network. It studies how to smartly direct traffic between multiple service locations so performance does not drop. The researchers propose new ways to upgrade 5G systems and tested their methods. Their solution made service connections 30% to 50% faster than current standard methods. -
Distributed User Association and Computation Offloading in UAV-Assisted Mobile Edge Computing Systems
This project focuses on using drones to help mobile devices process data faster. It finds the best way for devices to send tasks to drones while using the least energy. The study designs algorithms that let drones and devices work together efficiently. Tests show the approach saves energy compared to traditional methods. -
DRL-Based Distributed Task Offloading Framework in Edge-Cloud Environment
This project focuses on improving how tasks are handled in Internet of Things (IoT) systems. It combines cloud and edge computing to make task execution faster and more energy-efficient. The researchers created a smart system using deep learning to decide where tasks should run. Experiments show it saves energy, reduces delays, and works better than other methods. -
Energy Consumption and Time-Delay Optimization of DependencyAware Tasks Offloading for Industry 5.0 Applications
This project studies how to make mobile devices run complex tasks faster and use less energy by sending work to nearby servers. It looks at tasks that depend on each other and plans the order they should run. The project uses smart algorithms to choose which server handles each task. Tests show this method is better and more efficient than older approaches.
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How We Help You with Mobile Edge Computing Projects
At UniPhD, we provide complete guidance and support for Mobile 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.
Mobile Edge Computing Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Mobile Edge Computing research and thesis development. We offer fast-track dissertation writing services to help you complete your Mobile 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.
Mobile 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 Mobile Edge Computing final year project.
Mobile Edge Computing Research Support for PhD Scholars
UniPhD offers advanced Mobile 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.