Given a Vehicular Fog Computing (VFC) scenario, computing tasks with varying sensitivity levels must be offloaded while jointly balancing latency, energy, and trust requirements. Existing trust-based schemes often neglect some of these trade-offs or fail to adapt to task sensitivity and node reliability in dynamic vehicular settings. This paper proposes a trust-based task offloading (TTO) framework that employs a multi-dimensional trust model, combining Reputation, Experience, and Knowledge (REK) to compute a trust-aware overhead function that adapts to task requirements.We also incorporate computable encryption options, enabling task owners (TOs) to protect highly sensitive tasks. The interaction among TOs is modeled as a potential game, which implies the finite-improvement property and the existence of a Nash equilibrium, enabling distributed, self-adaptive decision-making that aligns individual incentives with overall system efficiency. This design allows vehicles to achieve an optimal balance between trust assurance and performance without centralized coordination. The efficiency of the framework against centralized approaches is validated by analyzing the price of anarchy. The simulation results confirm that TTO converges in 60 iterations for 100 nodes, achieving a 30-35% lower average delay and up to 25% lower total overhead compared to the considered baselines. These results indicate that TTO effectively balances trust, latency, and energy requirements.

Edge storage presents a viable data storage alternative for application vendors (AVs), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme's security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods. It also significantly reduces computational and communication overhead.

Edge storage is becoming an increasingly appealing alternative for data owners (DOs), offering benefits like decreased latency and minimized bandwidth usage compared to traditional cloud storage solutions. Nonetheless, stored data within edge servers (ESs) remains vulnerable to disruptions. Existing data integrity auditing schemes face challenges such as the costs of third-party auditors (TPA), unreliable and delayed audit results, and effective management of data inspection concerning time and energy consumption. To tackle these challenges, we introduce DIVO, a decentralized data inspection approach. DIVO leverages ESs as each others’ auditors, removing the necessity for a centralized party, thereby mitigating collision risks and potential biases in audit results. We propose a game-theoretic technique to efficiently manage data inspection and verification offloading to ESs. By formulating the decision-making issue as a strategic game for optimally allocating verification tasks among multiple ESs, we establish the presence of Nash equilibrium and design a strategy to attain it. Through comprehensive security and performance evaluations, DIVO has been shown to operate securely within the random oracle model while delivering notable efficiency improvements over recent methods. Our analysis highlights that DIVO surpasses a wide range of recent approaches in both communication and computation efficiency.