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The interconnected cloud computing paradigm is gaining considerable attention as a fundamental emerging model of cloud computing. It allows a wide range of interactions and collaborations across multiple service providers. Despite the potential advantages of interconnected clouds, establishing trust among participating parties is a challenging issue. In this paper, we introduce a lightweight trust management algorithm based on subjective logic (InterTrust) to promote trust in interconnected clouds. The experimental results demonstrate that InterTrust is capable of producing accurate trust information with significantly low execution time and high scalability compared to application of both the well-established trust management algorithm trust network analysis with subjective logic and no trust algorithm. [ABSTRACT FROM AUTHOR]

Edge devices in Internet of Things (IoT) applications can form peers to communicate in peer-to-peer (P2P) networks over P2P protocols. Using P2P networks ensures scalability and removes the need for centralized management. However, due to the open nature of P2P networks, they often suffer from the existence of malicious peers, especially malicious peers that unite in groups to raise each other's ratings. This compromises users' safety and makes them lose their confidence about the files or services they are receiving. To address these challenges, we propose a neural networkbased algorithm, which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious. In this paper, a neural network (NN) was chosen as the machine learning algorithm due to its efficiency in classification. The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems. [ABSTRACT FROM AUTHOR]