## 2 Related Work
### Mechanisms for Promoting Honest Behavior
Through experiments, the authors demonstrate the resilience and robustness of their approach against various malicious attacks, including self-promotion, bad-mouthing, ballot stuffing, opportunistic service, and on-off attacks. They further validate their results through a theoretical analysis of trust value convergence under different attack scenarios. To mitigate the impact of cooperative attacks, the authors propose an online countermeasure algorithm that analyzes the recommendation history recorded on the blockchain. This real-time algorithm is executed whenever fog nodes compute trust recommendations. Each fog node aggregates all recommendations for a particular IoT service provider and evaluates the minimum and maximum recommendation values. If the difference exceeds a predefined threshold, the provider is flagged as potentially engaging in a cooperative attack, and its recommendations are disregarded. The proposed protocol takes advantage of blockchain technology's strengths but also inherits its drawbacks. One major limitation is high resource consumption, as miners require substantial computing power to achieve consensus. [PERSON] et al. introduced in [23] a fuzzy logic-based model to filter dishonest recommendations in the SIoT. The model evaluates recommendations based on factors such as recommendation values, the sender's location coordinates, the time of submission, and social relationships. The proposed approach detects Sybil attacks by applying fuzzy classification to received recommendations, considering their Cosine Distance and temporal proximity. The underlying assumption is that recommendations that are similar in content, closely timed, and sent from nearby locations are likely generated by the same attacker conducting a Sybil attack. Once Sybil recommendations are identified, the remaining recommendations are classified based on the existing social relationships between the senders and the recommended object. These relationships include the Ownership Object Relationship (OOR), Co-Location Relationship (C-LOR), Co-Work Relationship (C-WOR), and Social Object Relationship (SOR). To further assess recommendation reliability, the model evaluates Internal Similarity (IS), which measures how closely each recommendation aligns with the median value of trusted witness objects within the same community. Simultaneously, it calculates the Degree of Social Relationship (DSR), which quantifies the strength of connections between the senders and the recommended object. The DSR is determined by the number of past transactions between objects, with greater interaction frequency indicating higher trustworthiness. Additionally, recommendations from specific communities, such as the OOR, carry more weight in the evaluation. The IS and DSR metrics play a crucial role in detecting good-mouthing and bad-mouthing attacks. If both the IS and DSR values are very low, the recommendation is deemed unreliable, suggesting that the sender may be attempting to manipulate trust through either a good-mouthing or bad-mouthing attack. In Vehicular Edge Computing (VEC), edge servers may request data from Autonomous Vehicles (AVs) to support intelligent applications such as Intelligent Transportation Systems (ITSs). However, economic concerns, including power consumption, create challenges in integrating data sharing within the dynamic VEC network. A promising alternative is shifting form data sharing to data trading, which incentivizes AVs to exchange their data for rewards. However, integrating data trading into edge servers introduces additional concerns related to security, privacy, and trust. To address these challenges, [PERSON] et al. propose in [24] a novel reputation management system for data trading that utilizes DRL. Their approach, called the Dynamic Selection of Trusted Sellers using Deep Deterministic Policy Gradient (DSTSDPG), dynamically adjusts a credibility score threshold to identify the most reliable data sellers among the available AVs.