Resource Allocation Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Resource Allocation 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 Resource Allocation Projects
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Deep Learning for Radio Resource Allocation Under DoS Attack
This project develops an intelligent system that helps wireless networks stay secure and efficient even under cyberattacks. It uses deep reinforcement learning to manage how sensors send data and save energy while resisting denial-of-service attacks. The system can also detect when attackers change their strategy and quickly adapt to it. This makes the network more reliable and resilient in real-time conditions. -
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. -
CFWS: DRL-based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers
This project focuses on making cloud data centers more energy-efficient and environmentally friendly. It uses artificial intelligence to decide when and where to move virtual machines across data centers. This helps reduce electricity use and carbon emissions while keeping services running smoothly. The system also minimizes unnecessary machine movements, saving time and energy. -
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. -
Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by PredictingSaaS Instance Configuration Using Deep Learning
This project improves how cloud systems manage resources for AI-based applications. It uses a smart method called Deep-Hill to predict the best setup for each service running in the cloud. By doing this, it saves energy, reduces costs, and improves how efficiently the system works. It shows how artificial intelligence can make cloud computing faster and more effective. -
Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization
This project focuses on improving cloud computing performance by balancing workloads across servers more efficiently. It introduces a new algorithm inspired by the Rock Hyrax to avoid uneven work distribution and reduce energy use. Tests show it speeds up processing by 10%–15% and lowers energy consumption by 8%–13%. The approach helps data centers run faster and use power more efficiently. -
Optimizing Cloud Performance: A Microservice Scheduling Strategy for Enhanced Fault-Tolerance, Reduced Network Traffic, and Lower Latency
This project focuses on improving how cloud applications run using microservices. It introduces a smart method to decide where each microservice should run, so the system works faster and avoids traffic jams. The method also balances the workload across servers and uses resources like CPU and memory efficiently. Tests show it performs better than existing approaches. -
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. -
Bee System-Based Self Configurable Optimized Resource Allocation Technique in Device-to- Device (D2D) Communication Networks
This project focuses on improving mobile network performance as traffic keeps growing. It uses a method inspired by how bees search for food to efficiently allocate resources between nearby devices. If the network is overloaded, extra resources and relay nodes are added to maintain good service. The approach improves user experience by reducing delay, saving energy, and supporting mobility. -
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. -
Method of Minimizing Energy Consumption for RIS Assisted UAV Mobile Edge Computing System
This project improves communication between drones and users in crowded cities. It uses smart reflective surfaces to help signals reach users better. The system also saves energy by adjusting the drone path, user power, and computing resources. Simulations show it works better than regular drone-based systems while keeping tasks stable. -
High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems
This project focuses on improving wireless communication using a technique called NOMA, which allows more data to be sent at once. The main challenge is deciding how to share channels and power efficiently. The researchers use a new method called a coherent Ising machine to quickly find good solutions. Their method is faster and more effective than existing approaches. -
Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO
This project studies how to improve wireless networks and mobile computing together. It combines a type of advanced antenna system with edge computing to share both communication and computing resources efficiently. The goal is to save user device power, reduce delays, and improve data transmission and computation. The researchers propose and test new methods to manage these resources for better performance. -
Joint Resource Management and Pricing for Task Offloading in Serverless Edge Computing
This project studies how to manage computing resources and prices in edge servers to help devices run tasks faster and save energy. The researchers model the problem as a game between the server and the devices. They develop fast algorithms that decide which tasks to run on the server, how to price them, and what apps to store. Their methods increase server revenue while reducing energy use for devices. -
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users
This project focuses on improving future wireless networks that serve two types of users at the same time. It uses federated learning to keep user data private while sending and receiving information efficiently. The study compares two communication methods to see which delivers better speed and reliability. The results show that the proposed methods work better than existing ones, especially when using the full-duplex approach. -
Meta Federated Reinforcement Learning for Distributed Resource Allocation
This project focuses on improving how cellular networks share resources like power and channels. Instead of relying on a central server, users handle much of the computation locally. The method helps save energy, reduce network traffic, and adapt quickly to changing conditions. It also allows users to collaborate, making the system faster and more efficient than traditional approaches. -
On the Impact of Re-Evaluation in 5G NR V2X Mode 2
This project studies how 5G technology helps cars communicate safely on the road. It looks at a feature called re-evaluation that checks for possible message collisions before sending data. The research finds that this feature works well for regular traffic but is less effective for irregular traffic. It also shows that while it can prevent some collisions, it adds extra work and may not greatly improve overall performance. -
Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5G RAN Network Slicing
This project focuses on improving how 5G networks share resources among multiple users. It introduces a way to give priority to different network slices so each gets fair performance. The study uses machine learning to speed up decisions about resource allocation. The results show that these methods work fast and accurately, but extra checks are needed when network conditions change or traffic is high. -
RAN Slicing with Inter-Cell Interference Control and Link Adaptation for Reliable Wireless Communications
This project focuses on improving 5G networks to handle two types of data traffic: one that needs very fast and reliable delivery, and another that carries large amounts of data. It proposes a new method to manage interference and resources without needing complex coordination between cells. The approach ensures reliable delivery and low delays for urgent data while using the network efficiently for high-data traffic. Simulations show it works better than existing methods. -
Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions
This project studies how future 6G networks can improve communication between vehicles and other devices on the road. It looks at ways to share network resources fairly and efficiently while avoiding interference and collisions. The study also explores new techniques like machine learning and edge computing to make vehicle communication faster and more reliable. Overall, it aims to make connected vehicles safer and more efficient in a hyperconnected 6G environment.
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