Changqing Xia

Bounding the Demand of Mixed-Criticality Industrial Wireless Sensor Networks

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.

Mixed Criticality Scheduling for Industrial Wireless Sensor Networks

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.

Convergecast scheduling and cost optimization for industrial wireless sensor networks with multiple radio interfaces

AbstractIndustrial wireless sensor networks have been widely deployed in many industrial systems. The main communication paradigm of such systems, known as convergecast, is to converge sensing data to a centralized manager.n The rapid and reliable data convergecast is essential to the industrial production. Multiple radio interfaces on a network device and convergecast scheduling algorithms can effectively reduce convergecast delay.n Existing works confine to the convergecast based on linear- and tree-based routing. Compared to the two routing schemes, graph routing is more reliable. Although the graph routing gains more popularity in industrial networks due to its better reliability, few works have addressed its temporality performance. On the other hand, the number of radio interfaces also impacts on the convergecast delay. In this paper, we present a holistic framework to solve how to use multiple radio interfaces to converge data. First, we propose a convergecast scheduling algorithm for industrial wireless sensor networks with multiple radio interfaces. Second, based on our proposed scheduling algorithm, we propose an optimal algorithm and a fast heuristic algorithm to minimize the number of radio interfaces under the temporality constraint of industrial production. Evaluations show that all our algorithms perform closely to the optimal solution.

Scheduling for Emergency Tasks in Industrial Wireless Sensor Networks

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.

A Hierarchical Data Transmission Framework for Industrial Wireless Sensor and Actuator Networks

A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to

Packet Aggregation Real-Time Scheduling for Large-Scale WIA-PA Industrial Wireless Sensor Networks

22%

Scheduling for MU-MIMO Wireless Industrial Sensor Networks

relative to existing methods.

Layout optimization for a long distance wireless mesh network: An industrial case study

The IEC standard WIA-PA is a communication protocol for industrial wireless sensor networks. Its special features, including a hierarchical topology, hybrid centralized-distributed management and packet aggregation make it suitable for large-scale industrial wireless sensor networks. Industrial systems place large real-time requirements on wireless sensor networks. However, the WIA-PA standard does not specify the transmission methods, which are vital to the real-time performance of wireless networks, and little work has been done to address this problem. In this article, we propose a real-time aggregation scheduling method for WIA-PA networks. First, to satisfy the real-time constraints on dataflows, we propose a method that combines the real-time theory with the classical bin-packing method to aggregate original packets into the minimum number of aggregated packets. The simulation results indicate that our method outperforms the traditional bin-packing method, aggregating up to 35% fewer packets, and improves the real-time performance by up to 10%. Second, to make it possible to solve the scheduling problem of WIA-PA networks using the classical scheduling algorithms, we transform the ragged time slots of WIA-PA networks to a universal model. In the simulation, a large number of WIA-PA networks are randomly generated to evaluate the performances of several real-time scheduling algorithms. By comparing the results, we obtain that the earliest deadline first real-time scheduling algorithm is the preferred method for WIA-PA networks.

Transmission scheduling for mixed-critical multi-user multiple-input and multiple-output industrial cyber-physical systems

Wireless sensor networks have been widely used in industrial environment. High reliability and real-time requirement are two main characteristics of wireless industrial sensor networks. Each flow can be transmitted to its destination on time by allocation node’s transmission slots. However, when transmission conflict occurs, the flow may miss its deadline and generate errors. To address this issue, we introduce MU-MIMO technique into industrial networks and propose a heterogeneous network model. Based on this model, we propose a slot analyzing algorithm (SAA) to guarantee the schedulability of networks. In considering of network cost, SAA also reduces the number of MU-MIMO nodes by slot analyzing. Evaluation results show the effectiveness and efficacy of our approach.

Deployment optimization for a long-distance wireless backhaul network in industrial cyber physical systems:

In the deployment of industrial wireless network, nodes can only be deployed in some special regions due to the restriction of the environment in the factory, thus failing to effectively elude occlusions, and restricting the performance of the network. Therefore, optimization should be made for layout of the network. An optimization is made on nodes layout in this paper based on the architecture of IEEE 802.11 WIFI Long-Distance multi-hop mesh networks. The optimization objectives are the network throughput and the network construction cost with the delay of traffics as constraint. For the scene with small network size, a hierarchical traversal method is adopted to get the optimal solution; and for that with large one, a hierarchical heuristic method is proposed to get the approximate solution. Finally, we carried out experiments via simulation and the scene constructed in the actual environment of the factory. The results show that the algorithms proposed in this paper can obtain effective solutions, and the heuristic algorithm has shorter computing time.