IoT, Volume 6, Issue 4 (December 2025) – 4 articles

Frame Replication and Elimination for Reliability (FRER) in Time-Sensitive Networking (TSN) enhances fault tolerance by duplicating critical traffic across disjoint paths. However, always-on FRER configurations introduce persistent redundancy overhead, even under nominal network conditions. This paper proposes a predictive FRER activation framework that anticipates faults using a Key Performance Indicator (KPI)-driven bidirectional Long Short-Term Memory (BiLSTM) model. By continuously analyzing multivariate KPIs—such as latency, jitter, and retransmission rates—the model forecasts potential faults and proactively activates FRER. Redundancy is deactivated upon KPI recovery or after a defined minimum protection window, thereby reducing bandwidth usage without compromising reliability. The framework includes a Python-based simulation environment, a real-time visualization dashboard built with Streamlit, and a fully integrated runtime controller. The experimental results demonstrate substantial improvements in link utilization while preserving fault protection, highlighting the effectiveness of anticipatory redundancy strategies in industrial TSN environments. Full article

The adverse propagation environment in underground coal mine tunnels caused by enclosed spaces, rough surfaces, and dense scatterers severely degrades reliable wireless signal transmission, which further impedes the deployment of IoT applications such as gas monitors and personnel positioning terminals. However, the conventional power enhancement solutions are infeasible for the underground coal mine scenario due to strict explosion-proof safety regulations and battery-powered IoT devices. To address this challenge, we propose singular value decomposition-based Lagrangian optimization (SVD-LOP) to minimize transmit power at the mining base station (MBS) for IRS-assisted coal mine wireless communication systems. In particular, we first establish a three-dimensional twin cluster geometry-based stochastic model (3D-TCGBSM) to accurately characterize the underground coal mine channel. On this basis, we formulate the MBS transmit power minimization problem constrained by user signal-to-noise ratio (SNR) target and IRS phase shifts. To solve this non-convex problem, we propose the SVD-LOP algorithm that performs SVD on the channel matrix to decouple the complex channel coupling and introduces the Lagrange multipliers. Furthermore, we develop a low-complexity successive convex approximation (LC-SCA) algorithm to reduce computational complexity, which constructs a convex approximation of the objective function based on a first-order Taylor expansion and enables suboptimal solutions. Simulation results demonstrate that the proposed SVD-LOP and LC-SCA algorithms achieve transmit power peaks of $20.8\mathrm{dBm}$ and $21.4\mathrm{dBm}$, respectively, which are slightly lower than the $21.8\mathrm{dBm}$ observed for the SDR algorithm. It is evident that these algorithms remain well below the explosion-proof safety threshold, which achieves significant power reduction. However, computational complexity analysis reveals that the proposed SVD-LOP and LC-SCA algorithms achieve $\mathcal{O}\left(N^3\right)$ and $\mathcal{O}\left(N^2\right)$ respectively, which offers substantial reductions compared to the SDR algorithm’s $\mathcal{O}\left(N^7\right)$. Moreover, both proposed algorithms exhibit robust convergence across varying user SNR targets while maintaining stable performance gains under different tunnel roughness scenarios. Full article

Real-time insect monitoring is essential for sustainable agriculture and biodiversity conservation. The traditional method of attracting insects to colored glue traps and manually counting the catch is time-intensive and requires specialized taxonomic expertise. Moreover, these traps are often lethal to pests and beneficial insects alike, raising both ecological and ethical concerns. Camera-based trap designs have recently emerged to lower the amount of manual labor involved in determining insect species, yet they are still deadly to the catch. This study presents the design and evaluation of a non-lethal acoustic monitoring system capable of detecting and classifying insect species based on their sound signatures. A first prototype was developed with a focus on low self-noise and suitability for autonomous field deployment. The system was initially validated through laboratory experiments, and subsequently tested in six rapeseed fields over a 25-day period. More than 3400 h of acoustic data were successfully collected without system failures. Key findings highlight the importance of carefully selecting each component to minimize self-noise, as insect sounds are extremely low in amplitude. The results also underscore the need for efficient data and energy management strategies in long-term field deployments. This paper aims to share the development process, design decisions, technical challenges, and practical lessons learned over the course of building our IoT sensor system. By outlining what worked, what did not, and what should be improved, this work contributes to the advancement of non-invasive insect monitoring technologies. Full article

The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. This extension enables the hierarchical aggregation of DTs across various abstraction levels. Traditional DT frameworks often lack mechanisms for dynamic composition at the level of entire systems. This is essential for modeling distributed systems in heterogeneous environments. UniTwin addresses this gap by grouping DTs into composite entities with an aggregation mechanism. The aggregation mechanism is demonstrated in a smart manufacturing case study, which covers the orchestration of a production line for personalized shopping cart chips. It uses modular DTs provided for each device within the production line. A System-Aggregated Digital Twin (S-ADT) is used to orchestrate the individual DTs, mapping the devices in the production line. Therefore, the production line adapts and reconfigures according to user-defined parameters. This validates the flexibility and practicality of the aggregation mechanism. This work contributes an aggregation mechanism for the UniTwin framework, paving the way for adaptable DTs for complex CPSs in domains like smart manufacturing, logistics, and infrastructure. Full article