Time sensitive networks support deterministic schedules over Ethernet networks. Due to their high determinism, high reliability and high bandwidth, they have been considered as a good choice for the backbone network of industrial internet of things. In industrial applications, the backbone network connects multiple industrial field networks together and has to carry massive real-time packets. However, the off-the-shelf time-sensitive network (TSN) switches can deterministically schedule no more than 1024 real-time flows due to the limited number of schedule table entries. The excess real-time flows have to be delivered by best-effort services because the switch only supports the two scheduling services. The best-effort services can reduce average delay, but cannot guarantee the hard real-time constraints of industrial applications. To make the limited number of schedule table entries support more real-time flows, first, we relax scheduling rules to reduce the requirement for schedule table entries and formulate the process of transmitting packets as a satisfiability modulo theories (SMT) specification. Then, we divide the SMT specification into multiple optimization modulo theories (OMT) specifications so that the execution time of solvers can be reduced to an acceptable range. Second, we propose fast heuristic algorithms that combine schedule tables and packet injection control to eliminate scheduling conflicts. Finally, we conduct extensive evaluations. The evaluation results indicate that, compared to existing algorithms, our proposed algorithm requires only one-twentieth the number of schedule entries to schedule the same flow set.

Wireless sensor networks (WSNs) have been widely used in industrial systems. Industrial systems demand a high degree of reliability and real-time requirements in communications. In many industrial WSNs applications, flows with different levels of criticality coexist in the system. When errors or exceptions occur, high-criticality flows must be guaranteed reliably and in real time. However, only a few works focus on mixed-criticality industrial systems. Concerning this issue, in this paper, we study mixed-criticality industrial systems and propose a supply/demand bound function analysis approach based on earliest deadline first scheduling. In addition, our method considers both source routing and graph routing. At the beginning, when the system is in low-criticality mode, source routing considers the schedulability of each flow. When errors or exceptions occur, the system switches to high-criticality mode, and network routing turns to graph routing to guarantee that important flows can be scheduled. By estimating the demand bound for mixed-criticality systems, we can determine the schedulability of industrial systems. Experiments indicate the effectiveness and efficacy of our approach.

Wireless sensor networks (WSNs) have been widely used in industrial systems. Their real-time performance and reliability are fundamental to industrial production. Many works have studied the two aspects, but only focus on single criticality WSNs. Mixed criticality requirements exist in many advanced applications in which different data flows have different levels of importance (or criticality). In this paper, first, we propose a scheduling algorithm, which guarantees the real-time performance and reliability requirements of data flows with different levels of criticality. The algorithm supports centralized optimization and adaptive adjustment. It is able to improve both the scheduling performance and flexibility. Then, we provide the schedulability test through rigorous theoretical analysis. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones.

Many critical applications necessitate indoor positioning, and the implementation of indoor positioning cannot be separated from various wireless signals. Among these, 5G-based indoor positioning is gaining increasing attention due to the wide coverage of 5G base stations and the stability of 5G signals. However, current positioning algorithms based on 5G signals require specialized measuring tools or information from telecommunication operators. Therefore, this paper proposes an indoor positioning algorithm, called 5G1M, that uses only a single 5G module and several commercial base stations already built outdoors. Besides these, 5G1M does not need any further assistance. We design 5G1M based on fingerprint positioning, which is less dependent on the environment and devices. First, 5G1M incorporates a Siamese network model with Ghost modules and squeeze-and-excitation blocks to reduce reliance on the scale of the fingerprint database and computational power while maintaining accuracy. Then, transfer learning is used to adapt to long-term environmental changes, and a trajectory-fitting method is employed to mitigate instantaneous signal fluctuations. The experimental result shows that in such a simplified positioning platform, 5G1M achieves a positioning error of 2.53 meters, improving accuracy by 29.7% compared to existing algorithms, and it also demonstrates faster convergence with fewer model parameters.

Wireless sensor networks (WSNs) are widely applied in industrial manufacturing systems. By means of centralized control, the real-time requirement and reliability can be provided by WSNs in industrial production. Furthermore, many approaches reserve resources for situations in which the controller cannot perform centralized resource allocation. The controller assigns these resources as it becomes aware of when and where accidents have occurred. However, the reserved resources are limited, and such incidents are low-probability events. In addition, resource reservation may not be effective since the controller does not know when and where accidents will actually occur. To address this issue, we improve the reliability of scheduling for emergency tasks by proposing a method based on a stealing mechanism. In our method, an emergency task is transmitted by stealing resources allocated to regular flows. The challenges addressed in our work are as follows: (1) emergencies occur only occasionally, but the industrial system must deliver the corresponding flows within their deadlines when they occur; (2) we wish to minimize the impact of emergency flows by reducing the number of stolen flows. The contributions of this work are two-fold: (1) we first define intersections and blocking as new characteristics of flows; and (2) we propose a series of distributed routing algorithms to improve the schedulability and to reduce the impact of emergency flows. We demonstrate that our scheduling algorithm and analysis approach are better than the existing ones by extensive simulations.