Unmanned Aerial Vehicles Final Year Projects

Byadmin Final Year Projects

Unmanned Aerial Vehicles Final Year Projects for BE, BTech, ME, MSc and MTech final year engineering students. Moreover, these Unmanned Aerial Vehicles projects give practical experience and help complete final-year submissions. Additionally, all projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Unmanned Aerial Vehicles Final Year Projects

  1. Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing
    This project focuses on making drone flights safer by detecting power lines. It uses a deep learning method called YOLO to find power lines in different shapes and positions. The algorithm improves detection by fixing missed lines and removing false ones. Tests showed it works better than older methods and can detect power lines in real time while the drone flies.
  2. Artificial Intelligence and Internet of Things for Sustainable Farming and Smart Agriculture
    This project focuses on using AI and IoT to make farming smarter and more sustainable. It looks at how these technologies can monitor crops and manage farming systems efficiently. The work reviews existing tools and proposes a framework to help farmers use AI and IoT effectively. The goal is to improve crop quality and handle large amounts of farming data more easily.
  3. Artificial Intelligence Technology in the Agricultural Sector A Systematic Literature Review
    This project explores how artificial intelligence is changing farming. It looks at tools like smart sensors, robots, and data analysis to monitor crops, soil, and water use. The study shows how AI can help farmers grow better crops, use resources efficiently, and increase profits. It also examines the benefits, challenges, and different AI methods used in modern agriculture.
  4. Cooperative Beamforming With Artificial Noise Injection for Physical-Layer Security
    This project focuses on improving security in future 6G wireless networks with many connected IoT devices. It proposes a method that combines cooperative beamforming and artificial noise to protect data from nearby eavesdroppers. The method works in a fully distributed way, reduces network overhead, and can significantly increase the secrecy of communications. Simulations show it can double the security performance compared to existing techniques, especially in high-risk scenarios.
  5. Uncovering Archaeological Sites in Airborne LiDAR Data With Data-Centric Artificial Intelligence
    This project uses drones and AI to help archaeologists find burial mounds more efficiently. It turns 3D LiDAR data into 2D images and trains an AI model to identify likely sites. A special technique reduces mistakes by checking if the shapes match real mounds. This makes it easier for archaeologists to focus on promising locations in the field.
  6. A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks
    This project focuses on improving how future 5G networks place and choose relay nodes that help users get better coverage and faster connections. It uses deep learning and reinforcement learning to make these decisions quickly, even when the network is crowded and complex. The study shows that these AI methods can greatly increase energy efficiency and data efficiency compared to traditional approaches. Overall, it provides a smarter way to manage communication resources in dense wireless networks.
  7. A Fresh Look at Routing Protocols in Unmanned Aerial Vehicular Networks A Survey
    This project studies how drones communicate with each other while moving in the air. It explains why choosing the best path for sending data is difficult because drone networks change quickly. The work compares different new routing methods and shows how well they perform. It also highlights remaining challenges and areas for future research in drone communication.
  8. Dispersal Foraging Strategy With Cuckoo Search Optimization Based Path Planning in Unmanned Aerial Vehicle Networks
    This project focuses on improving how drones (UAVs) move and communicate in complex environments like oceans. It proposes a new method that helps drones find the best paths while avoiding obstacles and efficiently sharing data. The approach also manages resources better and avoids getting stuck in poor solutions. Simulations show that this method performs better than existing techniques.
  9. Logistics UAV Air Route Network Capacity Evaluation Method Based on Traffic Flow Allocation
    The project creates a model to check how many delivery drones an air route network can safely handle. It studies how traffic, safety gaps, and efficiency affect drone movement. The model is solved using an improved search algorithm to find the best capacity. Tests show that the method gives reliable results and can be applied to real-world drone delivery routes.
  10. Methodology and Performance Assessment of Three-Dimensional Vehicular Ad-Hoc Network Simulation
    This project improves how we simulate communication between moving vehicles in cities. It adds realistic 3D factors, such as buildings and multi-floor environments, to make simulations closer to real traffic conditions. The authors show that 3D simulations can give very different results compared to basic 2D ones. They also propose methods to speed up the simulations so they run efficiently.
  11. Secure Relay Selection with Outdated CSI in Cooperative Wireless Vehicular Networks A DQN Approach
    This project focuses on improving wireless communication between vehicles. It develops smart methods to choose the best relay car for sending data, even when the network information is outdated. The system uses advanced machine learning techniques to reduce the chances of data being intercepted. Simulations show that the proposed methods perform much better than traditional approaches.
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