Search Results (96)

The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions. Full article

Autonomous driving systems (ADSs) increasingly rely on LiDAR sensors for perception. However, the resulting high-volume data places a strain on storage systems and network bandwidth and raises data-privacy concerns. We propose an IoT data engineering framework for processing, transmitting, storing, and retrieving high-volume LiDAR sensor data in in-vehicle systems that combines error-bounded compression and blockchain-based storage over in-vehicle Time-Sensitive Networking (TSN). With IEEE 802.1Qbv-based TSN scheduling, our framework supports deterministic delivery within the evaluated setup. It combines AES-GCM encryption, blockchain smart contracts, and InterPlanetary File System (IPFS) storage to support confidential, tamper-evident archival under the stated trust and threat model. Experimental evaluation on the KITTI dataset demonstrates that our BEDM framework reduces LiDAR data volume by 75.4%, contributing to a total network bandwidth reduction of 53.7%. The results demonstrate the feasibility and effectiveness of the integrated framework within the evaluated KITTI-based setup and single-switch TSN abstraction, and cross-scene and TSN traffic-sensitivity analyses further characterize its robustness. Full article

Time-Sensitive Networking (TSN) has emerged as a critical communication protocol for automotive Ethernet to support the high-bandwidth, real-time, and deterministic transmission requirements of next-generation in-vehicle networks. However, a clear and effective TSN mechanism combination tailored to the mixed and bursty traffic characteristics of automotive scenarios remains lacking. To address this issue, this paper proposes a combined TSN scheduling mechanism for automotive scenarios. The highest-priority traffic is scheduled by class-based Time-Aware Shaper (TAS), periodic bursty sensor traffic is shaped by Credit-Based Shaper (CBS), and medium-priority traffic adopts Multi-Cyclic Queueing and Forwarding (MCQF). Based on Compositional Performance Analysis (CPA), this paper derives the worst-case latency upper bound expressions for CQF streams and optimizes the schedulability analysis to reduce conservative errors. Simulation verifies that the theoretically calculated bounds cover the maximum simulation latency, and the optimized analysis reduces conservatism, with peak conservative error of 3.07% in the ring scenario and 10.59% in the automotive scenario. Compared with the strict priority and TAMCQF (a combination of TAS and Multi-CQF), the proposed mechanism combination suppresses the latency jitter of mixed traffic, mitigates long-duration blocking of medium-priority traffic caused by high-priority burst data, and provides reliable deterministic transmission guarantees for automotive in-vehicle networks. Full article

Currently, there is a trend in the energy sector towards the application of edge computing techniques to facilitate active monitoring of distribution networks. The adoption of this technique is crucial for applications involving distributed monitoring systems that require real-time data processing with low latency. An edge computing environment ensures an adequate response to two time-level response requirements. One for events that could trigger a serious problem in the distribution network, and a less demanding one for the management of energy. This article justifies and analyzes an architecture specifically designed to provide an adequate response to the two levels of time demand that set the procedure followed for the monitoring, storage and local diagnosis of several photovoltaic plants located on the same distribution network, with the aim of studying their joint production. One of the main contributions is related to the expansion of the capabilities of Phasor Measurement Units (PMUs) to monitor solar radiation or energy production perimeters by sector. The second major contribution is to guarantee the quality of the measurements and low latency in communications, using as a reference the IEEE C37.118-2011 synchrophasor standard in cooperation with the Time Sensitive Networking (TSN) synchronization protocol that guarantees simultaneity in distributed measurements. In short, a procedure is sought that allows a real-time response with the use of computing techniques very close to the origin of the measurements, guaranteeing exhaustive control from the moment the capture begins until the parameters are stored in a time series database. Full article

Driven by the rapid advancement of intelligence and connectivity, traditional distributed and signal-oriented automotive architectures are gradually being replaced by centralized, service-oriented architectures. In response to this transition, In-Vehicle Networks (IVNs) are expected to deliver high bandwidth, hard real-time performance, high reliability, and service-oriented capabilities. Data Distribution Service (DDS) and Time-Sensitive Networking (TSN) provide key technical support from the perspectives of service orientation and quality of service, respectively. Consequently, the integration of DDS and TSN has become a focal point in the field of IVNs. However, existing DDS message scheduling mechanisms cannot eliminate publishing time jitter, which prevents effective integration with deterministic scheduling mechanisms at the TSN layer, particularly the Time-Aware Shaper (TAS). To enable deterministic DDS communication in the DDS over TSN Architecture (DoTA), a Time-Triggered (TT) communication strategy based on message preemption and guard band mechanisms is proposed. This strategy is integrated into the flow controller of the DDS middleware. By scheduling a timed-event table, the publishing time of Time-Sensitive (TS) DDS messages is precisely controlled to align with the TAS mechanism. In addition, a schedulability analysis method is proposed to estimate the Worst-Case End-to-end Delay (WCED) of TS messages in DoTA. Experimental results from a physical testbed demonstrate that the proposed TT strategy can constrain the publishing time deviation of TS messages within 3 μs. When the TT strategy is jointly deployed with the TAS mechanism, both the end-to-end delay and jitter satisfy the requirements of safety-critical in-vehicle applications. Furthermore, the maximum deviation between the experimental results and the WCED estimated from the schedulability analysis is 15.4%. This indicates that the proposed method can effectively validate the feasibility of network designs and provide sufficient safety margins. Full article

Time-Sensitive Networking (TSN) with Cyclic Queuing and Forwarding (CQF) is a critical mechanism for ensuring deterministic forwarding. However, existing incremental schedulers typically rely on single-dimensional heuristics, which fail to address the coupled impact of traffic characteristics and spatiotemporal resource distribution. This limitation leads to suboptimal scheduling success, especially under complex topologies and high network loads. To address this, we propose TMCOA–MFS, a joint incremental scheduling framework that integrates the Triple-Mode Cooperative Optimization Algorithm (TMCOA) with a Multi-Feature Scheduling (MFS) strategy. The logic of our approach is twofold: First, to balance spatial resource distribution, we introduce the TMCOA—inspired by table-tennis offensive–defensive behaviors—to optimize path selection by minimizing port-load variance and escaping local optima through a three-mode population partition. Second, building upon the optimized spatial paths, the MFS strategy is employed to resolve temporal scheduling conflicts. By computing a composite priority score that accounts for path hops, offset configuration difficulty, and flow size, MFS enables a robust incremental offset search with integrated feasibility checking. Extensive simulations on benchmark functions and diverse TSN scenarios demonstrate that the TMCOA offers superior convergence and stability. More importantly, the integrated TMCOA–MFS framework significantly enhances scheduling success rates and load balancing, effectively overcoming the bottlenecks of high-load and topologically complex environments. Full article

The industrial metaverse is a digital twin space that integrates the real world with virtual environments through bidirectional synchronization. It supports critical services, such as time-sensitive machine control and large-scale collaboration, which require Time-Sensitive Networking and scalable Data Distribution Services. DDS, developed by the Object Management Group, provides excellent scalability and diverse QoS policies but struggles to guarantee transmission delay and jitter for time-critical applications. TSN, based on IEEE 802.1 standards, addresses these challenges by ensuring time-criticality. However, current research lacks comprehensive integration mechanisms for DDS and TSN, particularly from the viewpoints of semantics and system framework. Additionally, there is no adaptive QoS mapping converting the abstract DDS QoS policies to the sophisticated TSN QoS parameters. This paper presents a novel DDS-over-TSN framework that incorporates three key functions to address these challenges. First, Cross-layer QoS Mapping automates correspondences between DDS and TSN parameters, deriving technical constraints from standard documentation through retrieval-augmented generation. Second, Semantic Priority Estimation extracts substantial priority levels by utilizing language model embedding vectors as high-dimensional feature extractors. Third, Adaptive Resource Allocation performs dynamic bandwidth distribution for each priority level through reinforcement learning. Simulation results reveal over 99% mapping accuracy and 97% consistency in priority extraction. The applied Deep Reinforcement Learning paradigm allocated 99% of required resources to high-priority classes and reduced resource wastage by 15% compared to conventional methods. This methodology meets industrial requirements by ensuring both deterministic real-time performance and efficient resource isolation. Full article

With the rapid development of 5G communication technology, 5G networks are designed to achieve three major objectives: higher bandwidth, support for a greater number of connected devices, and lower latency. It is necessary to meet the requirements of the three primary 5G application scenarios: Enhanced Mobile Broadband, Massive Machine-Type Communications, and Ultra-Reliable and Low Latency Communications (uRLLC). To meet the stringent requirements for time synchronization and low latency, 5G is being integrated with Ethernet-based Time-Sensitive Networking (TSN) technologies. TSN plays an important role in achieving time determinism in uRLLC scenarios and ensures low-latency and high-reliability Ethernet communication through the transmission of time signals that are also known as the Precision Time Protocol. We applied TSN technology in the Institute of Electrical and Electronics Engineers 802.1Qbv standard and evaluated its transmission delay performance. Modifying the gate control list (GCL) to accommodate varying network traffic ensures low-latency transmission for high-priority traffic. We propose two GCL configurations for TSN that incorporate time-aware shaper to achieve efficient traffic scheduling. Full article

Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can provide low-latency, highly reliable, and deterministic communications over Ethernet, whereas IETF deterministic networking (DetNet) seeks to provide a complementary network layer to support ultra-low latency communications. DetNet, as standardized in the IETF, provides time-sensitive characteristics that assure extremely low packet loss and latency for ultra-reliable low-latency communications. This study develops a novel framework to enable 3GPP support for DetNet functionalities. First, the proposed framework seeks to support IP-based DetNet traffic and urgent data transmission in the network overload conditions of 3GPP 5G systems. Additionally, the proposed design supports DetNet service connectivity between non-DetNet and DetNet service areas. Based on simulation results, the proposed framework can guarantee deterministic latency requirements and critical data transmission for DetNet compared with conventional approaches. The proposed scheme can achieve more effective performance for moving DetNet devices. Full article

Accurate trajectory tracking is a key requirement in unmanned ground vehicles (UGVs) operating in autonomous driving, mobile robotics, and industrial automation. In wireless Time-Sensitive Networking (WTSN) scenarios, trajectory accuracy strongly depends on deterministic packet delivery, precise traffic scheduling, and time synchronization among distributed devices. This paper quantifies the impact of IEEE 802.1Qbv time-aware traffic scheduling on trajectory tracking accuracy in UGVs operating over Wi-Fi-enabled TSN networks. The analysis focuses on how misconfigured real-time (RT) and best-effort (BE) transmission windows, as well as clock misalignment between devices, affect packet reception and control performance. A mathematical framework is introduced to predict the number of correctly received RT packets based on cycle time, packet periodicity, scheduling window lengths, and synchronization offsets, enabling the a priori dimensioning of RT and BE windows. The proposed model is validated through extensive simulations conducted in an ROS–Gazebo environment, utilising Linux-based traffic shaping and scheduling tools. Results show that improper traffic scheduling and synchronization offsets can significantly degrade trajectory tracking accuracy, while correctly dimensioned scheduling windows ensure reliable packet delivery and stable control, even under imperfect synchronization. The proposed approach provides practical design guidelines for configuring wireless TSN networks supporting real-time trajectory tracking in mobile robotic systems. Full article

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

Time-Sensitive Networking (TSN), particularly the Time-Aware Shaper (TAS) specified by IEEE 802.1Qbv, is critical for real-time communication in Industrial Sensor Networks (ISNs). However, many TAS scheduling approaches rely on centralized computation and can face scalability bottlenecks in large networks. In addition, global-only schedulers often generate fragmented Gate Control Lists (GCLs) that exceed per-port entry limits on resource-constrained switches, reducing deployability. This paper proposes a two-phase distributed genetic-based algorithm, 2PDGA, for TAS scheduling. Phase I runs a network-level genetic algorithm (GA) to select routing paths and release offsets and construct a conflict-free baseline schedule. Phase II performs per-switch local refinement to merge windows and enforce device-specific GCL caps with lightweight coordination. We evaluate 2PDGA on 1512 configurations (three topologies, 8–20 switches, and guard bands ${\mathsf{\delta }}_{gb}\in \{0, 100, 200\} \mathrm{ns}$). At ${\mathsf{\delta }}_{gb}=0$ ns, 2PDGA achieves 92.9% and 99.8% CAP@8/CAP@16, respectively, compliance while maintaining a median latency of 42.1 $\mathsf{\mu }$s. Phase II reduces the average max-per-port GCL entries by 7.7%. These results indicate improved hardware deployability under strict GCL caps, supporting practical deployment in real-world Industry 4.0 applications. Full article

Accurate prediction of maximum flow latency is crucial for ensuring the efficient transport of latency-sensitive fronthaul traffic in packet-switched Xhaul networks while maintaining the reliable operation of 5G and beyond Radio Access Networks (RANs). Deterministic worst-case (WC) models provide strict latency guarantees but tend to overestimate actual delays, resulting in resource over-provisioning and inefficient network utilization. To address this limitation, this study evaluates a data-driven Quantile Regression (QR) model for latency prediction in Time-Sensitive Networking (TSN)-enabled packet-switched Xhaul networks operating under dynamic traffic conditions. The proposed QR model estimates high-percentile (tail) latency values by leveraging both deterministic and queuing-related data features. Its performance is quantitatively compared with the WC estimator across diverse network topologies and traffic load scenarios. The results demonstrate that the QR model achieves significantly higher prediction accuracy—particularly for midhaul flows—while still maintaining compliance with latency constraints. Furthermore, when applied to dynamic Xhaul network operation, QR-based latency predictions enable a reduction in active processing-node utilization compared with WC-based estimations. These findings confirm that data-driven models can effectively complement deterministic methods in supporting latency-aware optimization and adaptive operation of 5G/6G Xhaul networks. Full article

To ensure end-to-end Quality of Service (QoS) management in 5G-TSN converged networks, this paper proposes a dynamic weighted QoS mapping method based on Weighted Fuzzy C-Means and Three-Way Decisions (WFCM-TDwQM). The WFCM algorithm is employed to cluster Time-Sensitive Networking (TSN) flows based on their QoS attributes, reducing computational complexity. A three-way decision-based method is used to assign a reasonable and approximate set of 5G QoS Identifier (5QI) values to each cluster. Finally, dynamic weights are adjusted by considering QoS similarity and the residual load rate, enabling the system to adapt to network load changes. The experimental results show that, compared with three other mapping algorithm combinations, WFCM-TDwQM not only ensures end-to-end QoS consistency but also achieves better load balancing under varying network loads. Moreover, its mapping performance is evaluated under different network scenarios. Full article

Time-sensitive networking (TSN), a cutting-edge technology enabling efficient real-time communication and control, provides strong support for traditional Ethernet in terms of real-time performance, reliability, and deterministic transmission. In TSN systems, although time-triggered (TT) flows enjoy deterministic delay guarantees, audio video bridging (AVB) and best effort (BE) traffic still share link bandwidth through statistical multiplexing, a process that remains nondeterministic. This competition in shared memory switches adversely affects data transmission performance. In this paper, a priority queue threshold control policy is proposed and analyzed for mixed-critical traffic in time-sensitive networks. The core of this policy is to set independent queues for different types of traffic in the shared memory queuing system. To prevent low-priority traffic from monopolizing the shared buffer, its entry into the queue is blocked when buffer usage exceeds a preset threshold. A two-dimensional Markov chain is introduced to accurately construct the system’s queuing model. Through detailed analysis of the queuing model, the truncated chain method is used to decompose the two-dimensional state space into solvable one-dimensional sub-problems, and the approximate solution of the system’s steady-state distribution is derived. Based on this, the blocking probability, average queue length, and average queuing delay of different priority queues are accurately calculated. Finally, according to the optimization goal of the overall blocking probability of the system, the optimal threshold value is determined to achieve better system performance. Numerical results show that this strategy can effectively allocate the shared buffer space in multi-priority traffic scenarios. Compared with the conventional schemes, the queue blocking probability is reduced by approximately 40% to 60%. Full article