Blockchain‐Based Multiobjective Secure Task Offloading Strategy Utilizing Optimized Conditional Self‐Attention Generative Adversarial Network for Mobile Edge Computing in Internet of Vehicle

ABSTRACT The Internet of Vehicles (IoV) heralds a paradigm shift in vehicular communication by enhancing traffic efficiency, safety, and overall driving experiences through interconnectivity. However, real‐world IoV systems often face significant challenges. High computational demands for processing real‐time data in autonomous vehicles lead to rapid energy depletion, whereas vulnerabilities in communication networks can expose systems to cyberattacks. Although mobile edge computing (MEC) alleviates some of the issues by relocating computational resources closer to vehicles, it introduces critical hurdles. Frequent handovers due to high vehicle mobility result in service discontinuity and dynamic environments with dense vehicular networks complicate resource management and scalability. To tackle this, a blockchain‐based multiobjective secure task offloading strategy utilizing an optimized conditional self‐attention generative adversarial network (CSAGAN) enhanced by clouded leopard optimization (CLOA) is proposed (BMOSTO‐CSAGAN‐MEC‐IoV). The self‐attention mechanism of CSAGAN and the exploration–exploitation balance of CLOA prevent premature convergence by maintaining diverse search space, leading to optimized task scheduling. The model leverages fair proof of reputation (FPoR) for secure, decentralized task offloading ensuring data integrity and reliability. The proposed method attains 24.8% lower energy consumption, 26.2% lower latency, and 22.5% lower cost compared to existing methods when evaluated using IoV–synthetic dataset. These results demonstrate the model's effectiveness in enhancing IoV‐MEC system performance.