Since fog nodes are resource-constrained and imperfectly trusted heterogeneous devices, guaranteeing a real-time response to Internet of Things (IoT) tasks while optimizing system energy consumption remains a significant challenge. To overcome this, we first propose a acrlong DBC-enabled cloud–fog collaborative task scheduling architecture. Second, a task scheduling model is constructed to optimize system energy consumption and task deadline violation time while adhering to the IoT task response time restriction. Finally, two blockchain-enabled task scheduling algorithms are developed: 1) the reputation-based priority-aware algorithm (DB_RP) and 2) the accelerated ant colony system algorithm (DB_AACS). Extensive experiments are conducted to assess the proposed algorithm in four dimensions: 1) task completion rate; 2) system makespan; 3) system energy consumption; and 4) task deadline violation time. The experimental results demonstrate that the proposed algorithm is superior to the existing literature, and the acceleration strategy in DB_AACS is effective.

With the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core networks. Due to the instability of the mobile edge computing environment, it was difficult to guarantee the quality of service for users. To address this problem, a hybrid computation offloading framework based on Deep Deterministic Policy Gradient (DDPG) in IoT is proposed. The framework is a system consisting of edge servers and user devices. It is used to acquire the environment state through Software Defined Network technologies and generate the offloading strategy by Deep Deterministic Policy Gradient. The optimization objectives in this paper include the total system overhead of the mobile edge computing system, and considering both network load and computational load, an optimal offloading strategy can be obtained to enable users to obtain a better quality of service. Finally, the experimental results show that the algorithm outperforms the comparison algorithm and can reduce the system latency by 20%, while the network load and computational load are also more stable.