Summary Collaboration between IoT end‐devices and edge servers or idle devices enables offloading of computational tasks, providing an effective solution to address inherent limitations in computational resources, storage capacity, and energy efficiency. However, IoT network openness introduces security challenges, such as privacy breaches, data security, and “free‐riding” attack. Securing computational offloading is crucial for service quality and reliability. In this article, we introduce a comprehensive approach to address these challenges. First, trust metrics are performed at the level of resource requester and resource provider respectively, and the trust level of both parties is matched to reduce the adverse effects of malicious devices or servers on computing offloading. Then, a distributed decision‐making method based on game theory is designed to improve the performance of the algorithm and avoid the single‐point‐of‐failure problem through the joint participation of multiple edge servers in decision‐making. Finally, a blockchain‐based task offloading process control strategy is used to deal with the “free‐riding” attack. The results of simulation experiments show the effectiveness of our proposed offloading decision‐making method.

Summary The combination of edge computing (EC) and the Internet of Things is a hot research topic. And security is one of the most important problems to be solved. The trust measurement of devices is an effective way to solve the security problem, and the lack of a unified trust measurement model makes the untrusted devices destroy the quality of service. Establishing a reliable trust relationship between devices can effectively improve the security of the system. A decentralized trust measurement model for devices is proposed. First, a decentralized trust measurement framework is proposed, which combines EC with blockchain technology to establish a decentralized hierarchical structure; second, the credibility of devices is measured from multilevel and multi‐attribute; finally, a feedback trust filtering mechanism is designed to filter reliable feedback information, and then the trust between devices and the comprehensive trust of devices are calculated. Experiments and analysis show that the proposed decentralized trust measurement model can effectively measure the trust degree of devices and resist various malicious feedback attacks.