## 2 Related Work
### Mechanisms for Promoting Honest Behavior  
Besides [17], several other studies [18; 19; 20; 21; 22; 23; 24] propose mechanisms aimed at promoting honest behavior among agents during their interactions. These mechanisms serve as a basis for formulating a customized approach within the CA framework to counter various trust-related attacks, which we explore further in Section 8. A key requirement in large, dynamic, and heterogeneous networks--where node participation is constantly changing--is a mechanism for establishing a secure, authenticated channel between any two participating nodes to exchange sensitive information. [PERSON] et al. present in [18] a Bayesian trust model that probabilistically estimates node trust scores in an IoT environment, even in the absence of complete information. The authors propose a contract-theoretic incentive mechanism to build trust between devices in an ad hoc manner by leveraging locally adjacent nodes. Each IoT node independently stores and advertises its absolute trust score. The proposed effort-reward model motivates selected nodes to accurately report their trust scores and actively contribute to the authentication process, with rewards aligned to their actual trust levels. Unlike traditional blockchain-based solutions, this approach does not depend on cryptographic primitives or a central authority, and the final consensus is localized rather than being a universally shared state across all nodes in the system. To enhance content trust, [PERSON] et al. introduced in [19] TrustCoin, a smart trust evaluation framework for Information-Centric Networks (ICNs) in Beyond Fifth-Generation (B5G) networks, leveraging consortium blockchain technology. In this scheme, each TrustCoin user--whether a publisher/producer or a subscriber/consumer--is assigned credit quotas (i.e., coins) that reflect its reputation and serve as a measure of trust. A higher coin balance indicates greater credibility. Users must first register to obtain a legal identity and initial credit coins before they can request to publish content or report potentially malicious data. When content is published, a checking server verifies the publisher's credit balance on the blockchain to ensure it meets a predefined threshold. For content sharing, edge nodes authenticate user identities, while Deep Reinforcement Learning (DRL) is employed to assess content credibility. TrustCoin incorporates an incentive mechanism that encourages users to proactively publish trustworthy content. The system updates the credit coins of publishers and subscribers based on the content credibility determined by DRL-driven evaluations. Higher credibility results in rewards for publishers, reinforcing trustworthy behavior, while subscribers may face penalties. Conversely, if a publisher shares low-credibility content, they incur a penalty, while the subscriber is rewarded for correctly identifying and reporting it. In IoT networks, connected nodes may exhibit reluctance to forward packets in order to conserve resources such as battery life, energy, bandwidth, and memory. To address this issue, [PERSON] et al. propose in [20] a trust-based approach called HBST, which fosters cooperation by forming a credible community based on honesty. Unlike conventional methods that permanently remove selfish nodes from the network, HBST instead isolates them in a separate domain, preventing interactions with honest nodes while offering then an opportunity to improve their behavior and re-enter the network. The proposed model consists of two phases. In the first phase, credible nodes--those with a sufficient community reputation--are selected from the main network to form a \"credible community\". A node's reputation is determined by its honesty level, assessed using metrics such as interaction frequency, credibility, and community engagement. In the second phase, a social selection process appoints two leaders from this credible community based on factors such as seniority, cooperative behavior, and energy levels.