The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

In the Industrial Internet of Things (IIoT), Multi-access Edge Computing (MEC) emerges as a transformative paradigm for managing computation-intensive tasks, where task offloading plays an important role. However, due to the complex environment of IIoT, existing deep reinforcement learning-based schemes suffer from significant shortcomings in accuracy and convergence speed during model training when addressing the issue of task offloading. In this paper, to solve this problem, we propose an online task offloading scheme based on reinforcement learning, leveraging the double deep Q network (DQN) and dueling DQN with a prioritized experience replay mechanism, called the P rioritized experience-based D ouble D ueling DQN task offloading scheme (P-D3QN). P-D3QN enhances action selection accuracy using double DQN and mitigates Q-value overestimation by decomposing state and advantage using dueling DQN. Additionally, we adopt the prioritized experience replay mechanism to enhance the convergence speed of model training by selecting transitions that induce a higher training error between the evaluation network and the target network. Experimental results demonstrate that P-D3QN outperforms several state-of-the-art schemes, achieving a reduction of 21.0% in the average cost of the task and improving the completion rate of the task by 19.5%.

In this article, we present a work using a smartphone with an off-the-shelf WiFi router for human activity recognition with various scales. The router serves as a hotspot for transmitting WiFi packets. The smartphone is configured with customized firmware and developed software for capturing WiFi channel state information (CSI) data. We extract the features from the CSI data associated with specific human activities, and utilize the features to classify the activities using machine learning models. To evaluate the system performance, we test 20 types of human activities with different scales including seven small motions, four medium motions, and nine big motions. We recruit 60 participants and spend 140 hours for data collection at various experimental settings, and have 36 000 data points collected in total. Furthermore, for comparison, we adopt three distinct machine learning models, including convolutional neural networks (CNNs), decision tree, and long short-term memory. The results demonstrate that our system can predict these human activities with an overall accuracy of 97.25%. Specifically, our system achieves a mean accuracy of 97.57% for recognizing small-scale motions that are particularly useful for gesture recognition. We then consider the adaptability of the machine learning algorithms in classifying the motions, where CNN achieves the best predicting accuracy. As a result, our system enables human activity recognition in a more ubiquitous and mobile fashion that can potentially enhance a wide range of applications such as gesture control, sign language recognition, etc.

Fingerprint-based indoor localization methods have become an important technology because of their wide availability, low hardware costs, and the rapidly growing demand for location-based services. However, it is low precision of positioning and time-consuming for retraining the model when the fingerprint database has changed with new input samples. In this paper, we propose a novel intelligence localization scheme utilizing incremental learning without retraining models based on channel state information (CSI), namely ILCL. CSI phase data are extracted through a modified device driver, and we convert them into CSI images, which are the input to a convolutional neural network for training the weights in the offline stage. The estimated location is obtained by a probabilistic method based on a broad learning system (BLS) that can continue to train rapidly on new input data in the online stage. The ILCL architecture can be characterized as “deep” and “broad” and can further extract features. Experimental results confirm the superiority of ILCL compared with five existing algorithms in two real-world indoor environments with a total area is over 200 ${m}^{2}$ .

Data collection with mobile elements can greatly improve the load balance degree and accordingly prolong the longevity for wireless sensor networks (WSNs). In this pattern, a mobile sink generally traverses the sensing field periodically and collect data from multiple Anchor Points (APs) which constitute a traveling tour. However, due to long-distance traveling, this easily causes large latency of data delivery. In this paper, we propose a path planning strategy of mobile data collection, called the Dual Approximation of Anchor Points (DAAP), which aims to achieve full connectivity for partitioned WSNs and construct a shorter path. DAAP is novel in two aspects. On the one hand, it is especially designed for disconnected WSNs where sensor nodes are scattered in multiple isolated segments. On the other hand, it has the least calculational complexity compared with other existing works. DAAP is formulated as a location approximation problem and then solved by a greedy location selection mechanism, which follows two corresponding principles. On the one hand, the APs of periphery segments must be as near the network center as possible. On the other hand, the APs of other isolated segments must be as close to the current path as possible. Finally, experimental results confirm that DAAP outperforms existing works in delay-tough applications.