Jintao Wang

End-to-end delay analysis for mixed-criticality WirelessHART networks

WirelessHART, as a robust and reliable wireless protocol, has been widely-used in industrial wireless sensoractuator networks. Its real-time performance has been extensively studied, but limited to the single criticality case. Many advanced applications have mixed-criticality communications, where different data flows come with different levels of importance or criticality. Hence, in this paper, we study the real-time mixedcriticality communication using WirelessHART protocol, and propose an end-to-end delay analysis approach based on fixed priority scheduling. To the best of our knowledge, this is the first work that introduces the concept of mixed-criticality into wireless sensor-actuator networks. Evaluation results show the effectiveness and efficacy of our approach.

Cluster-Based Maximum Consensus Time Synchronization for Industrial Wireless Sensor Networks

Time synchronization is one of the key technologies in Industrial Wireless Sensor Networks (IWSNs), and clustering is widely used in WSNs for data fusion and information collection to reduce redundant data and communication overhead. Considering IWSNs’ demand for low energy consumption, fast convergence, and robustness, this paper presents a novel Cluster-based Maximum consensus Time Synchronization (CMTS) method. It consists of two parts: intra-cluster time synchronization and inter-cluster time synchronization. Based on the theory of distributed consensus, the proposed method utilizes the maximum consensus approach to realize the intra-cluster time synchronization, and adjacent clusters exchange the time messages via overlapping nodes to synchronize with each other. A Revised-CMTS is further proposed to counteract the impact of bounded communication delays between two connected nodes, because the traditional stochastic models of the communication delays would distort in a dynamic environment. The simulation results show that our method reduces the communication overhead and improves the convergence rate in comparison to existing works, as well as adapting to the uncertain bounded communication delays.

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.

Analyzing Multimode Wireless Sensor Networks Using the Network Calculus

The network calculus is a powerful tool to analyze the performance of wireless sensor networks. But the original network calculus can only model the single-mode wireless sensor network. In this paper, we combine the original network calculus with the multimode model to analyze the maximum delay bound of the flow of interest in the multimode wireless sensor network. There are two combined methods A-MM and N-MM. The method A-MM models the whole network as a multimode component, and the method N-MM models each node as a multimode component. We prove that the maximum delay bound computed by the method A-MM is tighter than or equal to that computed by the method N-MM. Experiments show that our proposed methods can significantly decrease the analytical delay bound comparing with the separate flow analysis method. For the large-scale wireless sensor network with 32 thousands of sensor nodes, our proposed methods can decrease about 70% of the analytical delay bound.

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.

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

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.

Scheduling for MU-MIMO Wireless Industrial Sensor Networks

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.

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

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.

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

Wireless sensor networks are widely used in industrial cyber-physical system installations, where high reliability and the need for real-time data are the two main characteristics. A large amount of real-time data can be transmitted to its destination on time using a reasonable periodic allocation of a node’s transmission slots. However, a flow may miss its deadline when flow conflicts occur. When such missed deadlines occur regularly, system performance may degrade, and when the flow is critical, such data losses can result in errors or cause disasters. To address this issue, we introduce multi-user multiple-input and multiple-output technology and a mixed-critical system into an industrial cyber-physical system. When an error occurs or when demand changes, the multi-user multiple-input and multiple-output nodes can switch their transmission mode, changing to a high-criticality configuration to meet the system’s new needs. Hence, we first propose a heterogeneous multi-user multiple-input and multiple-output system model. Based on this model, we propose a slot analyzing algorithm that guarantees system schedulability by reallocating slots for each node after replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By considering both system schedulability and cost, the slot analyzing algorithm also reduces the number of multi-user multiple-input and multiple-output nodes required. Then, to further reduce the number of multi-user multiple-input and multiple-output nodes in an industrial cyber-physical system, we propose a priority inversion algorithm that improves schedulability by adjusting slot allocations before replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By reducing the use of multi-user multiple-input and multiple-output nodes, the priority inversion algorithm achieves better performance than the slot analyzing algorithm when the system is in a high-criticality mode. Evaluation results show the effectiveness and efficacy of our approaches.

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

Industrial wireless networks are an important component of industrial cyber physical systems, and their transmission performance directly determines the quality of the entire system. During deployment, the nodes of an industrial wireless network can be deployed in only some specific regions due to physical environment restrictions in the factory; thus, occlusions are not always effectively circumvented and network performance is reduced. Therefore, this article focuses on the layout problem of the industrial backhaul network: a WiFi long-distance, multi-hop network. The optimization objectives were network throughput and construction cost, and the network delay was used as a constraint. For small networks, we propose a hierarchical traversal method to obtain the optimal solution, whereas for a large network, we used a hierarchical heuristic method to obtain an approximate solution, and for extremely large networks, we used a parallel interactive local search algorithm based on dynamic programming. Then, if the original network layout cannot meet the transmission demands due to traffic bursts, we propose a network bandwidth recovery method based on the Steiner tree to recover the network’s performance. Finally, the results of a simulation showed that the algorithms proposed in this article obtain an effective solution and that the heuristic algorithm requires less computing time.