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The proliferation of resource-constrained Internet-of-Things (IoT) and Industrial IoT (IIoT) devices has intensified the need for local, intelligent cybersecurity solutions that do not rely on cloud connectivity. This paper presents a TinyML-based Intrusion Detection System (IDS) framework designed for embedded edge controllers. Two machine-learning IDS models - a Convolutional Neural Network (CNN) and a Random Forest (RF) - were developed and evaluated on the TON-IoT dataset to identify malicious network traffic. Host-side evaluation established baseline performance, while the float-precision RF model was implemented as firmware and validated using hardware-in-the-loop emulation in the Renode RISC-V environment. The resulting binary occupies only 1.6 MB of Flash memory, achieving above 99% detection accuracy with 448 μs average inference latency. These results demonstrate that fully embedded, real-time intrusion detection is feasible within TinyML memory budgets, supporting IEC-62443 compliant anomaly monitoring for industrial devices.

Modelling plays a critical role in many engineering applications. Partial differential equations (PDEs) are ubiquitous, describing various physical phenomena such as fluid flow, electromagnetism, and quantum mechanics. Although some of these equations have analytical solutions, many require high-fidelity simulations of parametric PDEs. In general, high-fidelity simulations are computationally expensive and often infeasible for real-time or multi-query applications. This challenge has led to the development of reduced-order models (ROMs). Over the past few decades, ROMs have emerged as a practical solution for simulating, controlling, and optimizing large-scale and complex dynamical systems. This paper introduces a novel Calibrated Intrusive Reduced-Order Modelling (CIROM) approach for the efficient and accurate simulation of the one-dimensional Burgers' equation, employed as a canonical benchmark because it is a simplified fundamental partial differential equation that captures the behaviour of many real-world phenomena. The proposed method, combining the strengths of proper orthogonal decomposition (POD) and long short-term memory (LSTM) networks, effectively reduces computational complexity while addressing inherent instabilities in classical reduced-order models. Unlike traditional POD-ROMs, which often suffer from error accumulation and instability at high Reynolds numbers, the CIROM employs an iterative LSTM-based error correction mechanism to learn and compensate for truncation and projection errors. This study is benchmark-oriented and does not aim to provide a general PDE solver. The performance of the proposed method is rigorously evaluated across a broad range of Reynolds numbers, including interpolation and extrapolation scenarios, demonstrating robust extrapolation within moderate ranges. Comprehensive numerical experiments confirm that the CIROM outperforms both pure intrusive ROMs and purely data-driven LSTM models in terms of prediction accuracy, stability, and computational cost.

Atrial Fibrillation (AFib) is a common cardiac arrhythmia whose global prevalence has risen in recent years. If left untreated, AFib can lead to severe complications such as stroke and heart failure. Because AFib may occur without symptoms, continuous monitoring is critical for timely detection. This paper presents a low-cost Intelto detect AFib from Electrocardiogram (ECG) signals. The study evaluates the feasibility of deploying 1D-CNN models on resource-constrained edge devices and compares edge- and cloud-based computing architectures with respect to inference efficiency. Three 1D-CNN models of increasing complexity are designed, trained, and tested on datasets containing AFib and Normal Sinus Rhythm (NSR) segments. Two experiments are conducted to assess end-to-end delay and prediction time under a controlled experimental setup. The results demonstrate the potential for on-device AFib detection in constrained environments and provide practical insights into selecting suitable architectures for embedded deployment.

Industrial Internet of Things (IIoT) is changing the modern industrial processes through the integration of devices, sensors, and control systems, enabling real-time monitoring, automation, and datadriven decision-making. A significant challenge within IIoT systems, especially in control applications, is achieving guaranteed latency while ensuring high reliability. This paper presents a comprehensive review of recent research in IIoT, with a particular emphasis on the role of fifth generation (5G) wireless communication technologies. It includes an in-depth analysis of experiments conducted via real-world test-beds, providing insights into the performance of IIoT systems utilizing 5G technology. Additionally, the important support which has taken from different simulations and emulations in terms of library, protocols stacks and so on also discussed. We also assess how effectively these test-beds and platforms replicate real-world industrial environments and their capability to evaluate system performance under varying network configurations. Furthermore, we explore key technical parameters commonly used in IIoT research, including throughput, jitter, packet loss, spectral efficiency, and energy efficiency. This paper highlights both the benefits and limitations of employing test-beds for evaluating IIoT systems in industrial applications. It further examines the essential role of futuristic technologies, challenges and outlines research directions aimed at IIoT systems.

The convergence of Federated learning (FL) and Edge computing (EC) has emerged as an essential paradigm, particularly within the Industrial Internet of Things (IIoT) to enable the intelligent decision making. This work diligently examines the current state-of-the-art research at the intersection of FL, EC, and IIoT. An extensive review of the literature explores the diverse applications and challenges associated with this integration. The challenges range from privacy preservation and communication overhead to resource allocation. The incorporation of edge devices at which ensuring the federated learning in distributed manner helps to minimize energy consumption in IIoT, ultimately leads to a sustainable computing environment. By exploring the existing literature and research advancements, our goal is to highlight existing Edge-IoT software and hardware platforms and assess their usability in addressing challenges. In addition, we review existing recent frameworks, methodologies, and models employed to address these challenges, focusing on key performance matrices and its domain such as application, networking, and learning. We highlight the achievements and potential of FL and EC and underscore the need for tailored solutions to suit the unique demands of IIoT. Furthermore, we identify some of the major challenges as opportunities for future research, requires interdisciplinary collaboration and innovative algorithmic solutions. This work can help navigate through the challenges and unlock the full potential, contributing to the advancement of future IIoT applications.

Highlights: What are the main findings? A surrogate-based deep reinforcement learning (DRL) framework (FAE–CAE–LSTM) is proposed. Achieves higher accuracy compared to DMD, POD, and conventional autoencoders. Provides 5–10× computational speedup, enabling real-time flow control. Incorporates a CNN–MLP reward estimator that enhances policy performance. What is the implication of the main findings? Enables robust modeling of high-dimensional nonlinear flow dynamics. Reduces dependence on full-order PDE solvers, minimizing control delays. Scalable to turbulent and three-dimensional flow control problems. Offers broad applicability in fluid dynamics, energy systems, and engineering control. In cyber-physical systems governed by nonlinear partial differential equations (PDEs), real-time control is often limited by sparse sensor data and high-dimensional system dynamics. Deep reinforcement learning (DRL) has shown promise for controlling such systems, but training DRL agents directly on full-order simulations is computationally intensive. This paper presents a sensor-driven, non-intrusive reduced-order modeling (NIROM) framework called FAE-CAE-LSTM, which combines convolutional and fully connected autoencoders with a long short-term memory (LSTM) network. The model compresses high-dimensional states into a latent space and captures their temporal evolution. A DRL agent is trained entirely in this reduced space, interacting with the surrogate built from sensor-like spatiotemporal measurements, such as pressure and velocity fields. A CNN-MLP reward estimator provides data-driven feedback without requiring access to governing equations. The method is tested on benchmark systems including Burgers’ equation, the Kuramoto–Sivashinsky equation, and flow past a circular cylinder; accuracy is further validated on flow past a square cylinder. Experimental results show that the proposed approach achieves accurate reconstruction, robust control, and significant computational speedup over traditional simulation-based training. These findings confirm the effectiveness of the FAE-CAE-LSTM surrogate in enabling real-time, sensor-informed, scalable DRL-based control of nonlinear dynamical systems.

Low-power wireless Internet of Things (IoT) devices employ Time Slotted Channel Hopping (TSCH) Medium Access Control to achieve predictable timing behaviour. TSCH aims at collision-free scheduling by exploiting diversity over time (slots) and frequency (channels). However, existing works on performance and worst-case analysis are based on deterministic models, which lead to rather pessimistic non-realistic results, i.e. tools for probabilistic performance analysis of TSCH schedulers are still lacking. In this context, we devised a Stochastic Network Calculus model that enables to calculate end-to-end delays for specific traffic flows and (deadline) violation probability, building on Moment Generating Functions. We instantiate this SNC model and provide bounds for three widely used TSCH schedulers, namely Minimal Scheduling Function, Orchestra, and a custom collision-free scheduler, with different parameters such as radio duty-cycle, radio link quality, and traffic arrival rate. We demonstrate that our proposed model closely follows the simulation results, under different network scenarios.

RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

Applications of the Internet of Things (IoT), particularly within Industrial IoT, impose stricter reliability and efficiency requirements on low-power wireless technologies. This has driven the creation of new medium access protocols, such as Time Slotted Channel Hopping (TSCH). Recently, autonomous schedulers, which manage wireless links without node negotiation, are gaining popularity due to their lightweight and reliable operation. However, challenges arise with node mobility and dynamic traffic, as current schedulers use a static allocation method. To overcome this gap, we propose Forte, a hybrid scheduler that combines autonomous scheduling for basic connectivity with a centralized on-demand scheduler that allocates extra timeslots and frequency channels so that nodes adapt to the dynamic requirements. The centralized module formulates a Lyapunov optimization to guarantee queue stability while minimizing negotiation overhead and nodes’ duty-cycles. Forte outperforms the state-of-the-art by reducing packet end-to-end delay and increasing packet delivery ratio, all with minimal duty-cycle increase.

Future mines are expected to be operated by increasingly autonomous construction equipment, requiring dependable intercommunication between control centers, human operators, and construction machines such as excavators, drill rigs, and scrapers. Achieving stable, reliable and timely communications in such harsh and ever-changing environments is quite challenging. However, the changes in the three-dimensional (3D) topography of the mine are mostly predictable and scheduled through mine planning methods, which consequently can be used for radio communications network planning, namely to dimension, orientate and locate Base Station (BS) antennas in the mine field. In this context, we consider BSs to exist in the form of fixed cells or Cell on Wheel (CoW). The former is deployed in fixed locations throughout a long-term mine operation, while the latter is expected to be moved based on the changes in the topology of the terrain. We present an optimization framework that builds on an evolutionary algorithm to plan private 5G networks based on a given mine plan, featuring both fixed and movable base stations. We assess how the changing terrain affects the  wireless coverage on the mine's surface and demonstrate that, in certain scenarios, CoWs improve the average Signal-to-Interference & Noise Ratio (SINR) by 1 to 10 dB.