Nowadays, the applications related to Internet of connected vehicles (IoCV) have been greatly promoted by the roadside units (RSUs). To improve the transmission efficiency by the RSUs, 5G is introduced to the IoCV scenario for offering sufficient communication bandwidth. Generally, the traditional offloading destinations of the computing tasks in IoCV are the distant cloud servers, which consequently increases the response time of the tasks. Edge servers, placed together with macro base stations (MABSs) in 5G and RSUs, offer alternatives to host the tasks. However, the complicated locations of MABSs and RSUs make it difficult to distinguish the offloading destinations of the computing tasks in IoCV. In view of this, an adaptive computation offloading method, named ACOM, is devised for edge computing in 5G-envisioned IoCV to optimize the task offloading delay and resource utilization of the edge system. More specifically, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is fully leveraged to generate the available solutions. Then, the optimal offloading solution is obtained by utility evaluation. Eventually, the experimental results demonstrate the effectiveness of ACOM.

Cloud computing is widely used for its powerful and accessible computing and storage capacity. However, with the development trend of Internet of Things (IoTs), the distance between cloud and terminal devices can no longer meet the new requirements of low latency and real-time interaction of IoTs. Fog has been proposed as a complement to the cloud which moves servers to the edge of the network, making it possible to process service requests of terminal devices locally. Despite the fact that fog computing solves many obstacles for the development of IoT, there are still many problems to be solved for its immature technology. In this paper, the concepts and characteristics of cloud and fog computing are introduced, followed by the comparison and collaboration between them. We summarize main challenges IoT faces in new application requirements (e.g., low latency, network bandwidth constraints, resource constraints of devices, stability of service, and security) and analyze fog-based solutions. The remaining challenges and research directions of fog after integrating into IoT system are discussed. In addition, the key role that fog computing based on 5G may play in the field of intelligent driving and tactile robots is prospected.

As a critical branch of the Internet of Things (IoT) in the medicine industry, the Internet of Medical Things (IoMT) significantly improves the quality of healthcare due to its real-time monitoring and low medical cost. Benefiting from edge and cloud computing, IoMT is provided with more computing and storage resources near the terminal to meet the low-delay requirements of computation-intensive services. However, the service offloading from health monitoring units (HMUs) to edge servers generates additional energy consumption. Fortunately, artificial intelligence (AI), which has developed rapidly in recent years, has proved effective in some resource allocation applications. Taking both energy consumption and delay into account, we propose an energy-aware service offloading algorithm under an end-edge-cloud collaborative IoMT system with Asynchronous Advantage Actor-critic (A3C), named ECAC. Technically, ECAC uses the structural similarity between the natural distributed IoMT system and A3C, whose parameters are asynchronously updated. Besides, due to the typical delay-sensitivity mechanism and time-energy correction, ECAC can adjust dynamically to the diverse service types and system requirements. Finally, the effectiveness of ECAC for IoMT is proved on real data.

Visual anomaly detection plays an important role in industrial product quality inspection, but the scarcity of anomaly samples makes the training of supervised models extremely challenging. Many knowledge distillation-based unsupervised detection methods have been proposed recently; however, these anomaly detection methods mostly use similar or identical symmetric structures to build teacher–student (T–S) models, which hinders the different expression of anomalies between T–S networks. To address this issue, this article proposes an asymmetric T–S feature pyramid matching network (ATSN). This network consists of a teacher network, a student network, and a lightweight feature fusion module (FFM). The teacher network is constructed with ResNet18, and the student network comprises a U-shaped network consisting of an encoder and a decoder. In the knowledge transfer process, the encoder acts as an intermediate transmitter of information, and the decoder reconstructs the information transmitted by the encoder into multiscale features. The U-shaped design can effectively improve the accuracy of knowledge transfer, thus increasing the differences in anomalous representation via T–S feature pyramid matching. Additionally, an FFM is used to fuse the correct information from the multiscale feature representation in the encoder and the decoder, thereby compensating for the loss of key information in the decoder and further increasing the difference in anomalous representation. Experiments on the MVTec AD dataset show that our proposed method surpasses the current state-of-the-art methods.

With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions. Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs. Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power management strategies aiming at proposed challenges are introduced in detail with the description of their optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are discussed.