Optimized Federated Learning and Blockchain‐Based Crowd Sensing for Secure 5G Vehicular Networks

ABSTRACTIntelligent transportation systems (ITS) and the Internet of Vehicles (IoV) face significant challenges in ensuring data security, privacy, and low‐latency communication for vehicular crowd sensing. These challenges are exacerbated by rapid node mobility, bursty interactions, and vulnerabilities in existing systems, such as unauthorized data access, delayed message transmission, and inefficient blockchain consensus. To address these challenges, an Optimized Federated Learning and Blockchain‐Based Crowd Sensing for Secure 5G Vehicular Networks (OFGNN‐BTCS‐5G‐IoV) is proposed in this paper. Here, the system model is initialized, and the federated generative adversarial network (FGAN) is employed to select relevant active miners and transactions. The FGAN is optimized using the pelican optimization algorithm (POA) to determine optimal parameters to decrease uploading delay. Then the blockchain architecture is used to enhance data storage, transparent validation, and efficient miner selection by addressing privacy and scalability challenges using the Lightweight Proof of Game (LPoG) consensus mechanism. The proposed OFGNN‐BTCS‐5G‐IoV method is implemented in MATLAB, and the OFGNN‐BTCS‐5G‐IoV achieves 20.28%, 28.22%, and 29.27% higher active miner efficiency with 18.26%, 15.22%, and 12.27% lower latency when compared with existing methods. By using FGAN, bio‐inspired optimization, and blockchain, the OFGNN‐BTCS‐5G‐IoV offers secure and low‐latency vehicular crowd sensing for next‐generation ITS.