As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems.

Every year, many people around the world die because of mining accidents. Industrial Internet of Things (IIoT) can be employed to sense public safety hazards and provide early warning of accidents, thereby ensuring safe operations at underground mining, personnel positioning, and specific items supervision and emergency response. Real-time data anomaly detection can predict the probability of occurrence of the abnormal event. However, massive heterogeneous monitoring data, poor wireless environment and data spatio-temporal association have posed a serious challenge to data anomaly detection for underground mining. Existing methods are mostly concerned about single data or processing at cloud platform, with little regard for the time and space association. Focus on the accuracy and timeliness of data anomaly detection, a novel multi-source multi-dimensional data anomaly detection scheme based on hierarchical edge computing model is presented in this paper. Firstly, a hierarchical edge computing model is proposed to realize load balance and low-latency data processing at the sensor end and base-station end. Then a single-source data anomaly detection algorithm is designed based on fuzzy theory, which can comprehensively analyze the anomaly detection results of multiple consecutive moments. Finally, a multi-source data anomaly detection algorithm executed at the base-station end is designed to consider the sensing data associated attributes of time and space. Experimental results reveal that the proposed scheme has higher detection accuracy and lower processing delay compared with traditional solutions.

As a key technology in smart healthcare monitoring systems, wireless body area networks (WBANs) can pre-embed sensors and sinks on body surface or inside bodies for collecting different vital signs parameters, such as human Electrocardiograph (ECG), Electroencephalograph (EEG), Electromyogram (EMG), body temperature, blood pressure, blood sugar, blood oxygen, etc. Using real-time online healthcare, patients can be tracked and monitored in normal or emergency conditions at their homes, hospital rooms, and in Intensive Care Units (ICUs). In particular, the reliability and effectiveness of the packets transmission will be directly related to the timely rescue of critically ill patients with life-threatening injuries. However, traditional fault-tolerant schemes either have the deficiency of underutilised resources or react too slowly to failures. In future healthcare systems, the medical Internet of Things (IoT) for real-time monitoring can integrate sensor networks, cloud computing, and big data techniques to address these problems. It can collect and send patient’s vital parameter signal and safety monitoring information to intelligent terminals and enhance transmission reliability and efficiency. Therefore, this paper presents a design in healthcare monitoring systems for a proactive reliable data transmission mechanism with resilience requirements in a many-to-one stream model. This Network Coding-based Fault-tolerant Mechanism (NCFM) first proposes a greedy grouping algorithm to divide the topology into small logical units; it then constructs a spanning tree based on random linear network coding to generate linearly independent coding combinations. Numerical results indicate that this transmission scheme works better than traditional methods in reducing the probability of packet loss, the resource redundant rate, and average delay, and can increase the effective throughput rate.

The demand for improving productivity in manufacturing systems makes the industrial Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In IIoT systems, there is an increasing demand for different types of industrial equipment to exchange stream data with different delays. Communications between massive heterogeneous industrial devices and clouds will cause high latency and require high network bandwidth. The introduction of edge computing in the IIoT can address unacceptable processing latency and reduce the heavy link burden. However, the limited resources in edge computing servers are one of the difficulties in formulating communication scheduling and resource allocation strategies. In this article, we use deep reinforcement learning (DRL) to solve the scheduling problem in edge computing to improve the quality of services provided to users in IIoT applications. First, we propose a hierarchical scheduling model considering the central‐edge computing heterogeneous architecture. Then, according to the model characteristics, a deep intelligent scheduling algorithm (DISA) based on a double deep Q network (DDQN) framework is proposed to make scheduling decisions for communication. We compare DISA with other baseline solutions using various performance metrics. Simulation results show that the proposed algorithm is more effective than other baseline algorithms.

At present, sensor-based E-Healthcare systems are attracting more and more attention from academia and industry. E-Healthcare systems are usually a Wireless Body Area Network (WBANs), which can monitor or diagnose human health by placing miniaturized, low-power sensor nodes in or on patient’s bodies to measure various physiological parameters. However, in this process, WBAN nodes usually use batteries, and especially for implantable flexible nodes, it is difficult to accomplish the battery replacement, so the energy that the node can carry is very limited, making the efficient use of energy the most important problem to consider when designing WBAN routing algorithms. By considering factors such as residual energy of node, the importance level of nodes, path cost and path energy difference ratios, this paper gives a definition of Optimal Path of Energy Consumption (OPEC) in WBANs, and designs the Optimal Energy Consumption routing based on Artificial Bee Colony (ABC) for WBANs (OEABC). A performance simulation is carried out to verify the effectiveness of the OEABC. Simulation results demonstrate that compared with the genetic algorithm and ant colony algorithm, the proposed OEABC has a better energy efficiency and faster convergence rate.

Internet of Things (IoT) is a key enabler for many industrial applications. Through the IoT, the safety risk analysis and early warning management of the underground mining can effectively reduce the frequency of accidents and failures, to save the loss of personnel and property. Therefore, the safety warning of underground mining based on IoT is of great significance. However, underground industrial IoT requires the deployment of a large number of energy-constrained sensors and sensing units, and the wireless signals they send are lost due to data collisions, consuming node energy and reducing energy efficiency. Therefore, real-time reliable transmission of sensing data under energy-constrained conditions is critical for construction safety warnings in harsh industrial monitoring environments. Existing transmission schemes such as Wireless Hart, WiFi, etc., due to high energy consumption, cannot be directly applied to underground mining monitoring environments with energy-efficient requirements. Moreover, the competition-based ZigBee transmission protocol with low power consumption cannot guarantee the real-time reliable transmission of packets. Aiming at the real-time and reliability problems of data transmission under energy-limited conditions, a multi-radio multi-channel deterministic transmission scheme based on time slot division is proposed in this paper. Firstly, for the tree hybrid topology of monitoring networks, a joint multi-channel multi-slots scheduling problem is formulated, which is proved NP-Hard. Then, based on the greedy strategy, a lightweight pseudopolynomial time transmission scheduling scheme is proposed. It is also proved that under the premise that this problem can be scheduled, the heuristic algorithm can obtain the optimal result under the tree hybrid topology. Finally, a lot of practical experiments were conducted to verify the performance of the transmission scheme. The experimental results illustrate that compared with the traditional transmission scheme, the proposal has lower packet loss rate and transmission delay, which effectively reduces the energy consumption of the sensing devices.