Vehicular Ad Hoc Networks Final Year Projects with Source Code

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Vehicular Ad Hoc Networks Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Vehicular Ad Hoc Networks projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Vehicular Ad Hoc Networks Final Year Projects

  1. Cyber Physical Scheduling for Predictable Reliability of Inter Vehicle Communications
    This project focuses on making communication between vehicles more reliable. It creates a system that predicts and manages interference between vehicles. The method uses the locations and movement patterns of cars to schedule messages efficiently. Experiments show it improves reliability, speed, and reduces delays in vehicle communications.
  2. A Cross-Layer Solution for Contention Control to Enhance TCP Performance in Wireless Ad-Hoc Networks
    This project focuses on improving data transmission in wireless ad-hoc networks, which are used in IoT, vehicle networks, and sensor networks. It introduces a method called CSCC that helps control network congestion by adjusting how many packets are sent. The system ensures fair sharing of the network among users and reduces packet loss. Simulations show that CSCC performs better than traditional TCP methods in speed, fairness, and reliability.
  3. 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.
  4. A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning
    This project predicts which roadside unit a moving vehicle will connect to next. By knowing this early, the network can store the needed data in advance and reduce delay for users. The system uses learning methods that allow each roadside unit to make its own predictions. Tests in different cities show that the method predicts vehicle movement with high accuracy, even in complex traffic conditions.
  5. A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
    This project focuses on improving how clusters in data are evaluated. It introduces a new method called M2I that measures both how similar nodes are within a cluster and how separate clusters are from each other. This method works better than existing techniques, especially in dynamic networks like VANETs. Tests show that M2I gives very accurate results in both simulated and real-world data.
  6. A Novel Mechanism for Misbehavior Detection in Vehicular Networks
    This project focuses on making smart traffic systems safer. It studies networks where vehicles communicate with each other and identifies unusual behavior that could signal attacks. The system detects these threats, blocks harmful vehicles, and keeps a record of suspicious activity. Tests show it works accurately with very few mistakes.
  7. A Sensing Communication and Computing Approach for Vulnerable Road Users Safety
    This project focuses on making city roads safer for pedestrians and cyclists. It uses smart sensors in vehicles, on the road, and on devices like smartphones to detect possible accidents. The system predicts collisions early and warns both drivers and vulnerable road users. Tests show it works accurately and quickly, especially when using advanced networks like 5G and edge computing.
  8. A Survey of Sybil Attack Countermeasures in Underwater Sensor and Acoustic Networks
    This project studies how to make underwater sensor networks more secure. These networks are used for monitoring water quality, ocean life, and navigation, but they are vulnerable to attacks where fake nodes trick the system. The research reviews existing methods to prevent, detect, and reduce such attacks. It also highlights the challenges, limitations, and future opportunities for safer underwater communication.
  9. An Efficient Approach for the Detection and Prevention of Gray-Hole Attacks in VANETs
    This project focuses on improving the security of vehicular networks, where vehicles communicate with each other for safety and traffic management. The researchers studied a type of cyberattack called Gray-Hole Attack, where malicious vehicles drop data packets. They proposed a new method to detect and prevent these attacks by monitoring unusual packet behavior. Simulations showed that their method improved data delivery, reduced delays, and performed better than existing approaches.
  10. Cluster-Based Protocol for Prioritized Message Communication in VANET
    This project focuses on improving communication in Vehicular Ad Hoc Networks (VANETs), which connect vehicles wirelessly. Traditional methods are often too slow for moving vehicles, especially for road safety alerts. The proposed approach uses a clustering method to send emergency messages faster. Non-urgent information is stored temporarily to ensure urgent messages reach vehicles without delay.
  11. Detecting and Preventing False Nodes and Messages in Vehicular Ad-Hoc Networking VANET
    This project focuses on making vehicle communication networks safer. It detects and prevents fake vehicles and false messages on the road network. The system checks each vehicle’s profile and only accepts messages that meet certain rules. Simulations show that the approach successfully identifies and blocks fake nodes and messages in real time.
  12. Handover Reduction in 5G High-Speed Network Using ML-Assisted User-Centric Channel Allocation
    This project focuses on improving 5G network performance for high-speed road users like connected autonomous vehicles. It introduces a new channel allocation method called Vehicular Frequency Reuse (VFR) to reduce frequent handovers and improve connection reliability. A mobility management function separates high-speed and low-speed users using a simple velocity-based metric. Simulations show that this approach can reduce handovers by over 99% compared to traditional methods.
  13. Intelligent Driver Model-Based Vehicular Ad Hoc Network Communication in Real-Time Using 5G New Radio Wireless Networks
    This project focuses on improving communication between vehicles using 5G networks. It creates smart models for lane changes and accident avoidance to make vehicle movements more accurate and safe. The system is tested in different road shapes, and simulations show that vehicles communicate better and respond faster using 5G. Overall, it enhances traffic safety and efficiency through advanced vehicle-to-vehicle communication.
  14. Intelligent Urban Cities Optimal Path Selection Based on Ad Hoc Network
    This project focuses on making city traffic smarter using Internet of Things (IoT) and artificial intelligence. It uses smartphones as sensors to share traffic information with nearby devices, creating a temporary network even where the internet is weak. The system finds the best routes for drivers, reducing travel time by about 23% compared to traditional methods. It was tested in two urban cities and showed clear improvements in traffic flow.
  15. LCSS Based Sybil Attack Detection and Avoidance in Clustered Vehicular Networks
    This project focuses on making road networks safer for connected vehicles. It detects malicious vehicles, called Sybil nodes, that send fake messages and disrupt traffic systems. The method uses a cluster-based network and compares signal patterns to spot attackers. It improves detection speed, accuracy, and reduces false alarms compared to older approaches.
  16. 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.
  17. ML-RPL Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks
    This project uses machine learning to make wireless smart grid networks work better. It improves an existing routing protocol by predicting the best path for data to travel. The system helps data reach its destination more reliably and quickly. Tests show it delivers more packets with less delay, making the network more efficient.
  18. 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.
  19. Simulative Survey of Flooding Attacks in Intermittently Connected Vehicular Delay Tolerant Networks
    This project studies communication between vehicles and roadside units in networks where connections are often unreliable. It focuses on security problems, especially flood attacks, where malicious nodes overwhelm the network and waste resources. The research proposes a clear classification of these attacks and evaluates different methods to detect and reduce them efficiently. It also identifies open areas for further research on improving security in these networks.
  20. Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular Networks
    This project focuses on making vehicle networks smarter and safer. It uses a special network system called SDN to manage how vehicles connect and communicate with each other. The system improves security, protecting data from hackers, and uses machine learning to predict traffic changes and optimize network performance. Tests show it reduces delays and increases the reliability of messages between vehicles.
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