Fog computing has been proposed as an extension of cloud computing to provide computation, storage and network service in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and terminal device is considered. However, limited resources and low delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task scheduling model by considering the role of containers. Then, we construct a task scheduling algorithm to ensure that tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. Results showed that our proposed task scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of tasks in fog nodes.

With the rapid development of vehicle-to-everything communication technologies, many emerging compute-intensive in-vehicle applications have emerged. Vehicle edge computing (VEC) leverages the computational resources available at edge nodes to alleviate the strain on public network transmission and reduce task processing latency. However, the dynamic nature of the vehicle environment, the challenge of incentivizing vehicles to share idle resources, and the uncertainty surrounding the number of resources shared by vehicles present significant obstacles in designing task offloading and resource allocation methods for VEC systems. In this paper, we propose a hybrid offloading model wherein task vehicles can offload tasks to roadside units (RSUs) or other vehicles sharing resources. To maximize the benefits derived from task vehicles, RSUs, and shared resource vehicles, we first introduce an adaptive type selection algorithm (ALTS) for shared resource vehicles based on the multi-armed bandit (MAB) theory. Furthermore, we model the three-party interaction as a multi-stage Stackelberg game involving a computational resource lease contract. Experimental results demonstrate the superiority of the proposed ALTS algorithm over existing learning algorithms, thereby showcasing the effectiveness of the lease contract and the three-party transaction mechanism. Comparative experiments also reveal that integrating RSUs and idle vehicle resources offers better services compared to mechanisms relying solely on edge servers or shared resource vehicles.

Edge computing provides efficient and low-latency computing services for Internet of Things applications. Container virtualization technology is widely used as an indispensable key technology in edge computing. However, the creation of the container requires reading the corresponding image file. If the image file is not stored locally, it will take a lot of time to download, which increases the user’s extremely high service delay. Aiming at decreasing the download time of image files, we develop a two-stage optimization storage strategy of image files to decrease its download time based on edge computing. This strategy optimizes the image file placement in the initialization stage and the runtime stage, respectively. In the initialization stage, we propose a pseudo-polynomial time algorithm to filter all image files and select the image file combination, which best meets the capacity of the edge node for placement. In the runtime stage, we continue to optimize the local image repository based on the historical access records of the edge node. This operation can reduce the number of downloads of image files, thereby further reducing the user’s service delay. In addition, we created a real data set according to the service requirements and the structure of image files on the smart factory and the access records on the DockerHub. A large number of experiments are carried out based on the data set. Experimental results show that the two-stage optimization storage strategy can greatly reduce the download time of image files, thus reducing the service delay of edge nodes and improving the service quality of edge nodes.

WiFi-based indoor positioning has emerged as a crucial technology for enabling smart consumer electronic applications, particularly in large-scale buildings. The construction of WiFi fingerprint databases using received signal strength (RSS) is foundational due to its widespread deployment. However, achieving high positioning accuracy typically requires labor-intensive and time-consuming site surveys. While recent crowdsourcing methods have facilitated the collection of numerous RSS samples, these samples frequently lack labels and reliability in multi-scale building environments.In this paper, we design a novel semi-supervised and multi-task mean-teacher model (MTMT-DNN) to annotate crowdsourcing unlabeled multi-scale fingerprint samples. This method enables the construction of a comprehensive fingerprint database without requiring intensive manual effort or compromising positioning accuracy. Our key idea is to first develop a multi-task Deep Neural Network (MT-DNN) for simultaneously annotating building, floor, and intra-floor coordinate labels by leveraging their complementary information. Then we employ the mean-teacher semi-supervised learning to leverage additional unlabeled fingerprint data for further improving the annotating performance and reducing intensive manual effort. Finally, we train the MTMT-DNN model by developing two multi-task loss functions and ensuring consistency between them, thereby enhancing the reliability of the annotated crowdsourced fingerprints. We conducted real-world experiments in a 20,000 m2 site encompassing three multi-story buildings. The results demonstrate that our proposed method significantly reduces the workload of manually collecting labeled fingerprint samples. With only 20% of labeled fingerprints collected, we achieve 99% average annotation accuracy for building and floor labels and an average coordinates annotation error within 4 m.

One of primary and challenging difficulties in placing virtual machine (VM) onto physical machines in a cloud data center, is to balance the interests of both cloud service providers (CSPs) and cloud tenants, which are often expressed as minimizing the number of power-on servers and improving user experience respectively. However, the current research on VMP try to gathering the requested VMs on the fewest possible activated PMs for the purpose of reducing the number of power-on physical machines, without taking the experience of cloud tenants in to account. In this study, we define and formulate the VMP problem in cloud data center, and design a VMP strategy named Dependability based Distributed Virtual Machine Placement algorithm (D2VMP), with the placement goals of energy efficiency and dependability. To maintain the dependability of user requests D2VMP limits the number of virtual machines from the same request assigned on each physical machine. Furthermore, to ensure minimize the number of power-on physical machines, we propose to divide the data center into several clusters, and thus, different requests can reuse activated clusters. The experimental results demonstrate that proposed algorithm can effectively lower the power-on devices and maintain service-dependability.