Cyclic Queuing and Forwarding (CQF) is a key Time-Sensitive Networking (TSN) shaping mechanism that ensures bounded latency using a simple gate control list (GCL). Recently, variants of CQF, including Cycle Specific Queuing and Forwarding (CSQF) and Multi Cyclic Queuing and Forwarding (MCQF), have emerged. While popular TSN mechanisms such as the Time-Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), Credit-Based Shaper (CBS), and Strict Priority (SP) have been extensively studied, cyclic shapers have not been thoroughly evaluated. This paper presents a comprehensive analysis of CQF, CSQF, and MCQF, providing insights into their performance. We quantify delays through simulations and quantitative analysis on both synthetic and realistic networks. For the first time, we introduce an open-source OMNeT++ and INET4.4 based framework capable of modeling all three cyclic shaper variants. Our tool facilitates the validation of new algorithms and serves as a benchmark for cyclic shapers. Our evaluations reveal that MCQF supports diverse timing requirements, whereas CSQF, with its additional queue, often results in larger delays and jitter for some TT flows compared to CQF. Additionally, CSQF does not demonstrate significant advantages in TSN networks where propagation delays are less critical than in wide-area networks (WANs).

Cyclic queuing and forwarding (CQF) is a time-sensitive networking (TSN) shaping mechanism that provides bounded latency and deterministic Quality of Service (QoS). However, CQF’s use of a single cycle restricts its ability to support TSN traffic with diverse timing requirements. multi-cyclic queuing and forwarding (Multi-CQF) is a new and emerging TSN shaping mechanism that uses multiple cycles on the same egress port, allowing it to accommodate TSN flows with varied timing requirements more effectively than CQF. Despite its potential, current Multi-CQF configuration studies are limited, leading to a lack of comprehensive research, poor understanding of the mechanism, and limited adoption of Multi-CQF in practical applications. Previous work has shown the impact of time injection (TI), defined as the start time of time-triggered (TT) flows at the source node, on CQF queue resource utilization. However, the impact of TI has not yet been explored in the context of Multi-CQF. This article introduces a set of constraints and leverages domain-specific knowledge (DSK) to reduce the search space for Multi-CQF configuration. Building on this foundation, we develop an open-source genetic algorithm (GA) and a hybrid GA-simulated annealing (GASA) approach to efficiently configure Multi-CQF networks and introduce TI in Multi-CQF to enhance schedulability. Experimental results show that our proposed algorithms significantly increase the number of scheduled TT flows compared to the baseline simulated annealing (SA) model, improving scheduling by an average of 15%. Additionally, GA-SA (GASA) achieves a 20% faster convergence rate and lower time complexity, outperforming the SA model in speed, and efficiency.

This work considers a hybrid laser-battery powered uncrewed aerial vehicle (UAV) data collection system serving a passive Internet of Things deployment via monostatic backscatter communications. In this paper, we highlight the merits of the hybrid scheme over the laser only or battery only powered devices UAVs in terms of improved reach and durability. In addition, we study the laser energy consumption - destination battery level retention tradeoff optimization problem. Throughout this process, we optimize the single-rotor UAV’s three-dimensional trajectory, the UAV’s and the laser’s radiated power profiles, and the temporal battery usage profile while adopting path discretization. The resulting non-convex problem is solved via single-block successive convex approximation, for which novel bounds for the UAV propulsion energy, harvested energy, and collected data assuming a probabilistic line-of-sight channel model are derived. Finally, the simulation results show significant data collection gains, battery energy savings, and laser energy consumption reductions compared with a baseline scheme and highlight the complexity-optimality tradeoff.

Uncrewed aerial vehicles (UAVs) are expected to play a pivotal role in 6G networks due to their versatility and adaptability. One potential application for UAVs is wildfire coverage, as they can carry various sensors, including cameras and antennas. This study focuses on the multi-UAV trajectory optimization for wildfire coverage while satisfying multiple constraints, including the UAV dynamics, network connectivity, and limited energy batteries. The resulting complex optimization problem is time-varying and non-convex. To address this challenge, reinforcement learning, specifically the twin-delayed deep deterministic policy gradient algorithm, is adopted. A distributed learning procedure is devised to allow parallelization and significant reduction of the training time. The result is high coverage at standard flying altitudes with finite energy batteries.

The expansion of mobile sensors, like robots and uncrewed aerial vehicles (UAVs), across diverse applications such as remote sensing, monitoring, and communication relay, has been exponential. Yet, ensuring their safe and successful operation depends crucially on optimized deployment tailored to the application requirements while constrained by various limitations. This study focuses on the optimization of robot/UAV trajectories under these constraints. However, implementing constraints poses considerable challenges. To this end, a framework for constrained deployment optimization of wireless robotic swarms is proposed. This framework formulates as a quadratic-programming problem which utilizes Bézier curves to model trajectories and predict their states over a time horizon. Constraints are systematically categorized and embedded in the Bézier curve formulation. This framework offers ease of adoption to various scenarios and flexibility in accommodating different mobile sensor dynamics, constraints, and deployment strategies.

Fog Computing for Robotics and Industrial Automation, FORA, was a European Training Network which focused on future industrial automation architectures and applications based on an emerging technology, called Fog Computing. The research project focused on research related to Fog Computing with applicability to industrial automation and manufacturing. The main outcome of the FORA project was the development of a deterministic Fog Computing Platform (FCP) to be used for implementing industrial automation and robotics solutions for Industry 4.0. This paper reports on the scientific outcomes of the FORA project. FORA has proposed a reference system architecture for Fog Computing, which was published as an open Architecture Analysis Design Language (AADL) model. The tech-nologies developed in FORA include fog nodes and hypervisors, resource management mechanisms and middleware for deploying scalable Fog Computing applications, while guaranteeing the non-functional properties of the virtualized industrial control applications, and methods and processes for assuring the safety and security of the FCP. Several industrial use cases were used to evaluate the suitability of the FORA FCP for the Industrial IoT area, and to demonstrate how the platform can be used to develop industrial control applications and data analytics applications.

In this paper, we consider that critical control applications and Fog applications share a Fog Computing Platform (FCP). Critical control applications are implemented as periodic hard real-time tasks and messages and have stringent timing and safety requirements, and require safety certification. Fog applications are implemented as aperiodic tasks and messages and are not critical. Such applications need different approaches to guarantee their timing and dependability requirements. We formulate an optimization problem for the joint configuration of critical control and Fog applications, such that (i) the deadlines and Quality-of-Control (QoC) of control applications are guaranteed at design-time, (ii) the configuration is extensible and supports the addition of future new control applications without requiring costly re-certification, and (iii) the design-time configuration together with the runtime Fog resource management mechanisms, can successfully accommodate multiple dynamic responsive Fog applications. We evaluate our approach on several test cases assuming scenarios for hosting both Fog applications and future critical control applications. The results show that our approach generates extensible schedules which enables Fog nodes to handle Fog applications with a shorter response time and a larger number of future control applications.

Electric drives are used to control electric motors, which are pervasive in industrial applications. In this paper we propose enhancing the electric drives to fulfil the role of fog nodes within a Fog Computing Platform (FCP). Fog Computing is envisioned as a realization of future distributed architectures in Industry 4.0. We identify the system-level requirements of such an FCP, including requirements that are extracted from the current architecture of drives, which we consider as a baseline. These requirements are then used to design a system-level architecture, which we model using the Architecture Analysis & Design Language (AADL). We identify the "technology bricks" (components such as hardware, software, middleware, services, methods and tools) needed to implement the FCP. The proposed fog-based architecture is then used to implement a Conveyor Belt industrial use case. We evaluate the resulting use case on several aspects, demonstrating the usefulness of the proposed fog-based approach. By developing the electric drives as fog nodes, that we call fogification, new offerings like programmability, analytics and connectivity to customer Clouds are expected to increase the added value. Increased flexibility allows drives to assume a larger role in industrial and domestic control systems, instrumenting thus also legacy systems by using drives as the data source.

This paper presents JitterTime, a small Matlab toolbox for calculating the transient performance of a control system in non-ideal timing scenarios. Such scenarios arise in networked and embedded systems, where several applications share a set of limited and varying resources. Technically, the toolbox evaluates the time-varying state covariance of a mixed continuous/discrete linear system driven by white noise. It also integrates a quadratic cost function for the system. The passing of time and the updating of the discrete-time systems are explicitly managed by the user in a simulation run. Since the timing is completely handled by the user, any complex timing scenario can be analyzed, including adaptive scheduling and reconfiguration between different system modes. Three examples of how the toolbox can be used to evaluate the control performance of such time-varying systems are given.