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.

Energy savings optimization becomes one of the major concerns in the wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the limited nonrechargeable battery power. In this paper, we focus on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Following the principle of opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.

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.

Indoor localization and tracking have attracted growing attention because of its widely application for indoor Location Based Services(LBSs). However, for indoor localization, only with single positioning technology, the positioning accuracy and localizability are difficult to satisfy the requirement due to the complex and crowed indoor environment. An indoor cooperative localization and tracking algorithm (CLTA) based on grid is developed to solve above problems. The CLTA is divided into offline phase and online phase. In the offline phase, a cooperative localization fingerprint database is established based on reliable nodes. In the online phase, a region overlapping mechanism is used to narrow location area in multinetwork surroundings at first. Then we use a prediction mechanism to predict the mobile target position in order to further narrow the location area. At last, a cross grid strategy is used to update the data in fingerprint database if possible, aiming at improving the accuracy of localization. Simulation and experiment results show that the proposed algorithm is better than single network localization algorithm on localization and tracking performance.

Indoor localization has elicited increasing attention because it has been widely used in indoor location-based services. At present, many complex scenarios for indoor localization require position estimation not only in single-floor environments but also in multi-floor ones. However, existing works exhibit certain limitations in solving the problems such as high computational complexity and floor localization accuracy. In this study, a multi-floor identification system based on WiFi fingerprint database is designed to address these issues. This floor identification system is divided into offline and online phases. In offline phase, a localization fingerprint database is built based on WiFi nodes and a multi-floor identification model is proposed based on linear discriminant analysis (LDA), called MA_LDA. In online phase, the final floor number is determined, and the trained model is combined with the majority voting mechanism. After determining the floor number, an algorithm based on the k-nearest neighbor (KNN), called LL_KNN, is proposed to obtain the location information of a target on the floor. Real experiment results show that our system can identify the floor number by using only a little WiFi node fingerprint information rather than all the nodes to reduce computational complexity. It works efficiently and achieves high fault-tolerance performance compared with existing approaches in locating targets in a multi-floor environment.