Recently, smart cities, smart homes, and smart medical systems have challenged the functionality and connectivity of the large-scale Internet of Things (IoT) devices. Thus, with the idea of offloading intensive computing tasks from them to edge nodes (ENs), edge computing emerged to supplement these limited devices. Benefit from this advantage, IoT devices can save more energy and still maintain the quality of the services they should provide. However, computational offload decisions involve federation and complex resource management and should be determined in the real-time face to dynamic workloads and radio environments. Therefore, in this work, we use multiple deep reinforcement learning (DRL) agents deployed on multiple edge nodes to indicate the decisions of the IoT devices. On the other hand, with the aim of making DRL-based decisions feasible and further reducing the transmission costs between the IoT devices and edge nodes, federated learning (FL) is used to train DRL agents in a distributed fashion. The experimental results demonstrate the effectiveness of the decision scheme and federated learning in the dynamic IoT system.

Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.

Accurate division of the water-conducting fracturing zone (WCFZ) in the mining overburden serves as an important basis to evaluate the stability of coal mining under water bodies. Research on the WCFZ is conducive to controlling surface subsidence and realizing safe coal mining under water. Traditionally, the WCFZ is generally determined by field observation (liquid leakage method, borehole television, etc.) or empirical formula. Although these methods boast high accuracy, they are time-consuming and laborious and have some problems such as weak pertinence and a large value range. In this study, a mechanical model under the critical breakage condition of hard and soft strata was established on the basis of the specific geological and mining information of a mine. Besides, the stability condition for the broken strata to form the “masonry beam” structure and the deflection-based bending deformation formula of hard and soft strata were deduced, and the method of calculating the height of WCFZ based on the analysis of critical fracturing of soft and hard strata (hereafter referred to as the CFSHS-based height calculation method) was proposed. Furthermore, with reference to the results of specific engineering tests, the height of the WCFZ in the working face 15,101 of coal mine XJ was analyzed by means of theoretical analysis, numerical simulation and engineering verification, which verifies the rationality and practicability of the CFSHS-based height calculation method.