Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, wide coverage, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling data flows to meet their hard real-time requirements in industrial applications. In this paper, we first leverage a 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantee through time-frequency resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct a schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. For large-scale multi-cell 5G industrial systems with cloud radio access network (C-RAN) architecture, we further introduce a user association algorithm respecting the real-time requirements of individual user equipment (UEs). Extensive experimental studies show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.

Reproducing and extending research on TimeSensitive Networking (TSN) scheduling has become increasingly challenging, as most published methods lack open-source implementations. The few available implementations are often scattered across different programming languages and formats, forcing researchers to reimplement algorithms from scratch—a time-consuming and error-prone process that hinders fair comparison of methods and slows research progress. In this work, we present TSNKit, an open-source toolkit designed to address these challenges through: (i) standardized implementations of a broad set of representative scheduling algorithms with unified interfaces for integrating new methods; (ii) an end-to-end pipeline covering test case generation, scheduling, and simulation-based validation; and (iii) comprehensive benchmarking modules for reproducible performance evaluation. TSNKit enables researchers to reproduce published results, extend existing methods, and perform fair comparisons across algorithms. Our ongoing work extends TSNKit to support multiple traffic shapers beyond Time-Aware Shaping (TAS), improve benchmark efficiency through enhanced scheduling heuristics, and incorporate hardware-in-the-loop capabilities for seamless real-world deployment.

A growing trend in large-scale industrial system design is the integration of multiple real-time, mission-critical applications over shared network infrastructures to reduce hardware costs and improve scalability. Recent advances in network resource partitioning techniques provide practical mechanisms for managing these applications hierarchically while maintaining operational isolation. However, as system complexity increases, applications often exhibit multi-state behaviors that challenge the system's ability to meet stringent timing requirements - especially under static resource partitions. While dynamic resource reconfiguration can restore feasibility, it is typically costly and disruptive in industrial environments. To address this challenge, we propose a flexibility-aware network resource partitioning framework that introduces a novel metric - partition flexibility - to quantify how effectively a resource partition supports an application's state transitions. Using this metric, we develop efficient strategies for both static partition allocation and dynamic partition adjustment, with the goal of minimizing reconfiguration overhead. We validate our framework design through a real-world case study involving a NASA extra-terrestrial habitat system deployed on a time-sensitive networking (TSN) testbed. Extensive simulations further demonstrate that the proposed partitioning framework reduces$\mathbf{5 6. 4 \%}$reconfigurations compared to the state-of-the-art methods.

Time-Sensitive Networking (TSN) technology has been increasingly deployed in mission- and safety-critical industrial applications to achieve high throughput and deterministic communications. To provide stringent timing guarantee, TSN requires that network devices follow a predefined communication schedule for real-time end-to-end packet processing, involving both TSN bridges and end stations. Extensive efforts have been devoted on the TSN bridge design in the literature. Achieving TSN compatibility on the end stations (especially on COTS hardware), however is challenging due to their constrained resources. To fill this gap, this work presents a software-based open-source TSN end station design that i) enables ultra-low latency and nanosecond-level transmission accuracy based on DPDK, and ii) employs a novel multi-core scheduling framework to boost the throughput of real-time TSN traffic. Our proposed solution leverages existing COTS hardware and thus is more generic and cost-effective compared to existing hardware-centric solutions. We validate our design by developing a prototype end station and incorporating it in a real-world TSN testbed. Our extensive experiments demonstrate the efficiency and effectiveness of our design compared with other state-of-the-art solutions.

Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN's many traffic shapers due to its ability to achieve deterministic timing guarantees. Many scheduling methods for TAS shapers have been recently developed that claim to improve system schedulability. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding of their performance in diverse application scenarios. In this paper, we fill this gap by presenting a systematic review and experimental study on existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these works along with the specific problems they aim to solve, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform an extensive evaluation on 17 representative solutions using both high-fidelity simulations and a real-life TSN testbed, and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these studies, we identify the limitations of individual scheduling methods and highlight several important findings. We expect this work will provide foundational knowledge and performance benchmarks needed for future studies on real-time TSN scheduling.

5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the URLLC requirements due to the non-negligible transmission delays introduced during the spectrum resource grant process. To address this problem, 5G defines a grant-free transmission scheme, namely configured grant (CG) scheduling, for uplink (UL) traffic to pre-allocate spectrum resource to user equipments (UEs). This paper studies CG scheduling for periodic URLLC traffic with real-time and collision-free guarantees. An exact solution based on Satisfiability Modulo Theory (SMT) is first proposed to generate a feasible CG configuration for a given traffic set. To enhance scalability, we further develop an efficient graph-based heuristic consisting of an offset selection method and a multicoloring algorithm for spectrum resource allocation. Extensive experiments are conducted using 3GPP industrial use cases to show that both approaches can satisfy the real-time and collision-free requirements, and the heuristic can achieve comparable schedulability ratio with the SMT-based approach but require significantly lower running time.

Resource virtualization is a promising technique that has been increasingly deployed in industrial automation systems to support multiple time-critical applications sharing the same physical resources. Extensive studies have been reported on how to perform real-time virtualization on computing resources. However, when applying virtualization techniques on network resources (especially for real-time wireless networks), node dependency among applications, wireless channel contention and stringent end-to-end timing requirements of the real-time flows in the network pose severe challenges. To address this problem, this paper formulates the network virtualization problem for multi-hop multi-channel real-time wireless networks (RTWNs). We first present a Satisfiability Modulo Theory (SMT)-based exact solution to capture the constraints posted by each application's resource interfaces and node dependency graphs. A novel supply graph (SG)-based partitioning framework, SGP, is then proposed to determine the resource partitions for individual applications. SGP uses supply graph to maintain compliance with the regularity constraints while efficiently allocating resources. Experimental results from both a real-world testbed and extensive simulations show that SGP can achieve comparable success ratio with the SMT-based exact solution but reduce the computational overhead significantly.

Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling industrial data flows to meet their hard real-time requirements. In this paper, we first leverage a real-world 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantees through time-frequency-space resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct the schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. To evaluate the performance of 5G-TPS, we present a case study of a motion control panel use case and perform extensive experiments. The results show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.

In order for real-time wireless networks (RTWNs) to achieve desired Quality of Service (QoS) for real-time sensing and control, effective packet scheduling algorithms play a critical role, especially in the presence of unexpected disturbances. Most existing solutions in the literature focus either on static or dynamic schedule construction to meet the desired QoS requirements, but have a common assumption that all wireless links are reliable. However, this assumption is not realistic in real-life settings. To address this drawback, this paper introduces a novel reliable dynamic packet scheduling framework, called RD-PaS. RD-PaS can not only construct static schedules to meet both the timing and reliability requirements of end-to-end flows in RTWNs, but also construct new schedules rapidly to handle abruptly increased network traffic induced by unexpected disturbances while minimizing the impact on existing network flows. Through judiciously sharing time slots among tasks, RD-PaS can significantly reduce the number of required time slots to meet the system reliability requirement and improve the network throughput. The functional correctness of the RD-PaS framework has been validated through its implementation and deployment on a real-life RTWN testbed. Extensive simulation-based experiments have also been performed to evaluate the effectiveness of RD-PaS, especially in large-scale network settings.

Industrial wireless networks (IWNs) are being increasingly deployed in the field to serve as the network fabrics for various industrial Internet-of-Things (IIoT) applications. Given that IWNs typically operate in noisy and harsh environments, frequently occurring network dynamics post huge challenges for IWN resource management especially when the network scales up. Existing centralized and distributed network management solutions either suffer from large communication overhead and time delay, or introduce schedule collisions which unnecessarily degrade the system performance. To address these problems, this work proposes a novel HierArchical Resource Partitioning framework (HARP), to provide dynamic resource management in IWNs. By hierarchically partitioning and allocating resources for the links in the network, HARP enables distributed collision-free resource allocation. HARP enables rapid adjustment of the partitions in the presence of network dynamics with modest communication overhead. The effectiveness of HARP is validated and evaluated through both simulation studies and testbed experiments on a 50-node multi-channel multi-hop 6TiSCH network.

With the rapid growth of industrial Internet of Things (IIoT) applications, real-time wireless networks (RTWNs) are playing an increasingly important role in providing realtime, reliable, and secure communication services for these applications. A key challenge in RTWN management is to ensure real-time Quality of Services (QoS), especially in the presence of unexpected external (i.e., application-side) and internal (i.e., network-side) disturbances. This paper presents a novel framework, DS-PaS, to determine the packet transmission schedule for multi-channel multi-hop RTWNs at the data link layer in a distributed and dynamic fashion. DS-PaS is able to (i) handle external disturbances, (ii) support spatial reuse, (iii) meet deadlines of all critical tasks, and (iv) minimize the number of dropped non-critical packets. To avoid transmission collisions when using inconsistent information in a distributed framework, DS-PaS incorporates several key advances in both the data-link layer protocol and algorithm design so that individual nodes can build on-line schedules with only local interference information. Extensive evaluation based on both testbed implementation and simulation validates the correctness of the DS-PaS design and demonstrates its effectiveness compared to the state of the art.

The fifth-generation (5G) cellular networks are increasingly considered for industrial applications, such as factory automation systems. In 5G networks, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC) are two essential services. eMBB services require high data rates with some lower bounds while URLLC traffic is subject to strict latency and reliability requirements. Existing approaches to scheduling coexisting eMBB and URLLC traffic all assume that URLLC traffic preempts eMBB traffic immediately upon arrival, which can adversely impact the achievable eMBB data rates. Furthermore, none of the prior work considers guaranteeing minimum data rate requirements imposed on certain eMBB traffic. This paper proposes a new model to capture the URLLC and eMBB requirements and introduces a novel framework, QoSG-RA, to perform network resource allocation for coexisting eMBB and URLLC traffic. QoSG-RA builds on a hybrid offline/online approach which performs offline resource allocation to ensure the Quality of Service (QoS) requirements of eMBB and URLLC traffic to be satisfied and online resource allocation to maximize fairness on the data rates among eMBB traffic based on runtime information. QoSG-RA is able to (i) meet latency and reliability requirements of URLLC traffic, and (ii) maximize the data rates for eMBB traffic in a fair way while fulfilling their minimum data rate requirements. Experimental results demonstrate the effectiveness of QoSG-RA compared to the state-of-the-art.

Malicious attackers may disrupt the safety of autonomous systems through compromising sensors to feed wrong measurements to the controller. This article proposes attack-resilient sensor fusion that combines local sensor readings and shared sensing information from multiple sources. The method results in higher resilience against sensor attacks through jointly considering sensing noise and uncertain communication delay. To be specific, we first identify the considerable impact of the delay on determining attacked sensors. Second, we present a novel two-dimensional abstract sensor model, where each measurement is augmented as a probabilistic interval based on the convolution of the noise and delay. Third, we propose a fusion algorithm that admits the fused value with highest joint probability distribution of the intervals to tolerate corrupted measurements. Finally, we demonstrate the effectiveness of our method in a vehicle-platoon case study using extensive simulations and testbed experiments.

Industrial Internet of Things (IIoT) systems aim to interconnect a large number of heterogeneous industrial sensing and actuation devices through both wired and wireless communication technologies and further connect them to the Internet to achieve ubiquitous sensing, computing and control services [1]. As a representative IIoT technology, 6TiSCH [2] targets at gluing together the 802.15.4e data link layer (offering industrial performance in terms of timing, reliability and power consumption) and an IP-enabled upper layer stack to achieve both deterministic network performance and seamless integration with Internet services. In recent years, 6TiSCH has been receiving increasing attentions from both industry and academia. We have witnessed its wide deployment in many industrial domains, including advanced manufacturing, industrial process control, smart grids, and healthcare.

The past decade has witnessed the rapid development of real-time wireless technologies and their wide adoption in various industrial Internet-of-Things (IIoT) applications. Among those wireless technologies, 6TiSCH is a promising candidate as the de facto standard due to its nice feature of gluing a real-time link-layer standard (802.15.4e, for offering deterministic communication performance) together with an IP-enabled upper-layer stack (for seamlessly supporting Internet services). 6TiSCH's built-in random slot selection scheduling algorithm, however, often leads to large and unbounded transmission latency, thus can hardly meet the real-time requirements of IIoT applications. This paper proposes an adaptive partition based scheduling framework, APaS, for 6TiSCH networks. APaS introduces the concept of resource partitioning into 6TiSCH network management. Instead of allocating network resources to individual devices, APaS partitions and assigns network resources to different groups of devices based on their layers in the network so as to guarantee that the transmission latency of any end-toend flow is within one slotframe length. APaS also employs a novel online partition adjustment method to further improve its adaptability to dynamic network topology changes. The effectiveness of APaS is validated through both simulation and testbed experiments on a 122-node multi-hop 6TiSCH network.

Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been playing a more critical role in providing real-time, reliable, and secure communication services for such applications. A key challenge in RTWN management is how to ensure real-time Quality of Services (QoS) especially in the presence of unexpected disturbances and lossy wireless links. Most prior work takes centralized approaches for handling disturbances, which are slow and subject to single-point failure, and do not scale. To overcome these drawbacks, this article presents a fully distributed packet scheduling framework called FD-PaS . FD-PaS aims to provide guaranteed fast response to unexpected disturbances while achieving minimum performance degradation for meeting the timing and reliability requirements of all critical tasks. To combat the scalability challenge, FD-PaS incorporates several key advances in both algorithm design and data link layer protocol design to enable individual nodes to make on-line decisions locally without any centralized control. Our extensive simulation and testbed results have validated the correctness of the FD-PaS design and demonstrated its effectiveness in providing fast response for handling disturbances while ensuring the designated QoS requirements.

Along with the rapid development and deployment of real-time wireless network (RTWN) technologies in a wide range of applications, effective packet scheduling algorithms have been playing a critical role in RTWNs for achieving desired Quality of Service (QoS) for real-time sensing and control, especially in the presence of unexpected disturbances. Most existing solutions in the literature focus either on static or dynamic schedule construction to meet the desired QoS requirements, but have a common assumption that all wireless links are reliable. Although this assumption simplifies the algorithm design and analysis, it is not realistic in real-life settings. To address this drawback, this paper introduces a novel reliable dynamic packet scheduling framework, called RD-PaS. RD-PaS can not only construct static schedules to meet both the timing and reliability requirements of end-to-end packet transmissions in RTWNs for a given periodic network traffic pattern, but also construct new schedules rapidly to handle abruptly increased network traffic induced by unexpected disturbances while minimizing the impact on existing network flows. The functional correctness of the RD-PaS framework has been validated through its implementation and deployment on a real-life RTWN testbed. Extensive simulation-based experiments have also been performed to evaluate the effectiveness of RD-PaS, especially in large-scale network settings.

Real-time wireless networks (RTWNs) are fundamental to many Internet-of-Things (IoT) applications. RTWNs typically apply time-division multiple access (TDMA)-based media access control mechanisms and often demand deterministic end-to-end packet delivery to meet the given quality of service (QoS) requirements. Packet scheduling in an RTWN thus plays a critical role for achieving the desired performance but is a challenging problem especially when the RTWN is large and must deal with multiple disturbances (i.e., unexpected events causing abrupt workload changes associated with certain sensing tasks) occurring concurrently. This paper introduces a novel distributed dynamic packet scheduling framework, D2-PaS. D2-PaS is capable of processing disturbances and minimizes the number of dropped packets while ensuring that all critical events due to disturbances are handled by their deadlines. As a distributed approach, D2-PaS constructs schedules locally at individual nodes, which significantly reduces the amount of schedule-related information to be broadcast by the gateway. As a dynamic approach, D2-PaS applies a lightweight packet dropping algorithm to determine on-line at the gateway which packets can be dropped in response to disturbances and disseminate this information to the network. D2-PaS has been implemented on a multi-hop RTWN testbed to validate its applicability on hardware and a popular RTWN stack. Both testbed measurements and extensive simulation results demonstrate the effectiveness of D2-PaS.

A demonstration of 6TiSCH Industrial IoT network handling network resource management and data collection and analytics.

Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been playing a more critical role in providing real-time, reliable and secure communication services for such applications. A key challenge in RTWN management is how to ensure real-time Quality of Services (QoS) especially in the presence of unexpected external and internal disturbances. Most prior work takes a centralized approach for handling disturbances, which is slow and subject to single-point failure, and does not scale. To overcome these drawbacks, this paper presents a fully distributed packet scheduling framework called FD-PaS. FD-PaS aims to provide guaranteed fast response to unexpected disturbances while dropping a minimum number of packets for meeting the deadlines of all critical tasks. To combat the scalability challenge, FD-PaS incorporates several key advances in both algorithm design and data link layer protocol design to enable individual nodes to make on-line decisions locally without any centralized control. Our extensive simulation and testbed results have validated the correctness of the FD-PaS design and demonstrated its effectiveness in providing fast response for handling disturbances.

We designed a crossdevice testing and reporting system, called cross-device testing and reporting system (CD-TRS), to facilitate the functional validation of protocol and application design in large-scale RTWNs. CD-TRS leverages the nice property of RTWNs that all devices in the network are fully synchronized. By specifying and retrieving events and device status from multiple devices in the runtime simultaneously, CD-TRS can further assemble them into a network-wide report on the detailed system behavior by aligning the records according to their associated network timestamps (or absolute slot number (ASN) in most RTWNs). By comparing this runtime network behavior report with the required protocol and application specifications, abnormal device/network behavior can be observed and their root cause(s) can be effectively located. This thus can significantly reduce the complexity of RTWN testing and reporting. In the following, we first describe the overall architecture of CD-TRS, and then demonstrate how CD-TRS helps with the functional validation of D2-PaS, a distributed and dynamic packet scheduling framework we recently developed for handling disturbances in real-time wireless networks.

Real-time wireless networks (RTWNs) are fundamental to many Internet-of-Things (IoT) applications. Packet scheduling in an RTWN plays a critical role for achieving desired performance but is a challenging problem especially when the RTWN is large and subject to unexpected disturbances from the environment. Few solutions exist to tackle this challenge but they suffer serious limitations. This paper introduces a novel distributed dynamic packet scheduling framework, D2-PaS. D2-PaS aims to minimize the number of dropped packets while ensuring that all critical events due to disturbances are handled by their deadlines. D2-PaS builds on a number of observations that help reduce the scheduling overhead, and thus is efficient and scalable. Besides extensive simulation, D2-PaS has been implemented on an RTWN testbed to validate its applicability on real hardware. Both testbed measurements and simulation results confirm the effectiveness of D2-PaS. Compared to the best known work, D2-PaS reduces packet drop rates by 65% and 90% on average and in the best case, respectively, and also achieves 100% success for all the randomly generated task sets.