Unmanned Aerial Vehicles Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Unmanned Aerial Vehicles 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 Unmanned Aerial Vehicles Projects
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GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems
This project studies how advanced AI, called GANs, can trick wireless communication systems. It shows that fake signals made by GANs can look almost exactly like real ones. The research tested this on modern multi-carrier signals used in networks. The results show that receivers can be fooled 99.7% of the time, revealing a serious security risk. -
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. -
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 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. -
Application of Artificial Potential Field Method in Three-Dimensional Path Planning for UAV Considering 5G Communication
This project improves how drones fly while staying connected to computers using 5G networks. It plans drone paths in three dimensions by considering areas with the best 5G signal. The method also helps drones avoid getting stuck in tricky spots. Tests show it increases signal strength along the path, even if the route becomes slightly longer. -
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. -
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. -
JDACO: Joint Data Aggregation and Computation Offloading in UAVEnabled Internet of Things for Post-Disaster Scenarios
This project studies how drones can help IoT devices work better after disasters. The drones collect data and provide computing power to support decision-making. The researchers created a method that combines data collection and computation to save energy and reduce delays. Tests show their approach works faster, uses less energy, and serves more devices than existing 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. -
A Multi-Layer Information Dissemination Model and Interference Optimization Strategy for Communication Networks in Disaster Areas
This project studies how information spreads in communication networks used during disasters. It creates a model to understand how messages travel between nodes and how network interference affects this process. The work also finds the best network setup to reduce interference and deployment cost. Simulations show the model accurately predicts information flow and helps improve disaster communication. -
Advancing UAV Communications A Comprehensive Survey of CuttingEdge Machine Learning Techniques
This project reviews how machine learning is used with drones in mobile networks. It explains how drones can act like flying users or mini base stations. The study looks at how AI can help improve coverage, energy use, and network performance. It also explores new AI methods and how they can work with cloud or edge computing. -
Capacity Analysis of UAV-to-Ground Channels With Shadowing: Power Adaptation Schemes and Effective Capacity
This project studies how a drone can send data to a receiver on the ground more efficiently. It looks at different ways the drone can adjust its power to improve the communication channel. The researchers derived formulas to measure how much data can be sent under these strategies. They also checked their results using computer simulations. -
Cell-Free UAV Networks With Wireless Fronthaul: Analysis and Optimization
This project studies how drones can be used to improve wireless networks without relying on traditional cell towers. Users send data to drones, which then forward it to a central processing point using wireless links. The work looks at different ways to share these links and optimizes drone positions and power settings to get the best network performance. The main benefit comes from placing the drones in the right 3D locations. -
Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation
This project focuses on making drones navigate on their own without human control. It uses a type of artificial intelligence called deep reinforcement learning to plan safe and efficient paths. The method splits the area into regions and gives rewards based on distance and obstacles to guide learning. Tests show it makes drones learn faster and avoid getting stuck in poor paths. -
Double-Faced Active Intelligent Reflecting Surfaces-Assisted Symbiotic Radio Communications
This project focuses on improving energy efficiency for Internet-of-Things devices in next-generation wireless networks. It uses a special system called symbiotic radio with a double-sided intelligent surface to help devices send and receive signals more efficiently. The researchers designed an algorithm to optimize the system so it works well while using less power. Simulations show it performs better than other similar methods. -
Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms
This project focuses on making groups of drones work together even when GPS or location data is missing. The team developed methods for drones to organize themselves, join, or split from a group automatically. Their approach keeps the drones connected and communicating in challenging places like forests or indoors. Tests show the system works reliably and quickly in realistic conditions. -
Efficient Deployment Strategies for Network Localization With Assisting Nodes
This project focuses on improving how well devices can know their location in wireless networks with limited infrastructure. It studies where to place helper nodes to make location estimates more accurate. The work develops methods to choose near-optimal positions for these nodes. Testing shows that placing helper nodes carefully can significantly improve location accuracy. -
Enabling Flexible Arial Backhaul Links for Post Disasters A Design Using UAV Swarms and Distributed Charging Stations
This project focuses on using drones and charging stations to provide reliable data links to areas affected by disasters. The goal is to plan the positions and number of drones and stations to either reduce costs or improve service quality. The team developed smart methods to find near-optimal solutions that work almost as well as the best possible design. Simulations show that these methods are effective and practical for real-world use. -
Fairness Enhancement of UAV Systems With Hybrid Active-Passive RIS
This project studies wireless communication using drones to connect with multiple users. It uses smart surfaces that can actively or passively reflect signals to improve coverage. The goal is to make the connection fair for all users by optimizing the drone’s path, signal direction, and surface settings. The results show that even a few active elements on the surface can greatly improve communication speed compared to fully passive surfaces. -
LOS Analysis for Localization in High Frequency Systems
This project studies how terahertz wireless signals travel and how obstacles affect them. It focuses on keeping a clear line-of-sight between transmitters and receivers to improve connection quality and accurate location tracking. The research analyzes different setups of base stations and environments to see how signal reliability can be maximized. It shows that the number and height of base stations play an important role, especially in dense areas.
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