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.

Recent advances in deep learning have fostered a transition from the traditional, bit-centric paradigm of Shannon’s information theory to a semantic-oriented approach, emphasizing the transmission of meaningful information rather than mere data fidelity. However, black-box AI-based semantic communication lacks structured discretization and remains dependent on analog modulation, which presents deployment challenges. This paper presents a new semantic-aware digital speech communication system, named Hybrid-DeepSCS, a stepping stone between traditional and fully end-to-end semantic communication. Our system comprises the following parts: a semantic encoder for extracting and compressing structured features, a standard transmitter for digital modulation including source and channel encoding, a standard receiver for recovering the bitstream, and a semantic decoder for expanding the features and reconstructing speech. By adding semantic encoding to a standard digital transmission, our system works with existing communication networks while exploring the potential of deep learning for feature representation and reconstruction. This hybrid method allows for gradual implementation, making it more practical for real-world uses like low-bandwidth speech, robust voice transmission over wireless networks, and AI-assisted speech on edge devices. The system’s compatibility with conventional digital infrastructure positions it as a viable solution for IoT deployments, where seamless integration with legacy systems and energy-efficient processing are critical. Furthermore, our approach addresses IoT-specific challenges such as bandwidth constraints in industrial sensor networks and latency-sensitive voice interactions in smart environments. We test the system under various channel conditions using Signal-to-Distortion Ratio (SDR), PESQ, and STOI metrics. The results show that our system delivers robust and clear speech, connecting traditional wireless systems with the future of AI-driven communication. The framework’s adaptability to edge computing architectures further underscores its relevance for IoT platforms, enabling efficient semantic processing in resource-constrained environments.

Device-to-device (D2D) communication is an innovative technique within cellular networks, holding great potential for future wireless communication systems, particularly for the Internet of Things (IoT). One key advantage of D2D communication is its ability to alleviate cellular traffic congestion, as many IoT applications may prefer to use cellular networks due to interoperability and compatibility. In this paper, we propose a novel opportunistic channel access model and adaptive power control strategy for a cluster of IoT transmitter-receiver pairs (referred to as D2D pairs) underlying the cellular uplink. Our objective is to assess the feasibility of leveraging this proposed model for offloading IoT traffic. To this end, we evaluate the model’s performance under various parameter settings, taking into account practical limitations such as total power restrictions and minimum spectral efficiencies. Additionally, we compare the results against two baseline power control strategies. Our findings indicate that instead of constructing a new and dedicated network architecture for IoT applications, it is possible to offload IoT-generated traffic through D2D communication underlying the cellular uplink. This approach not only avoids detrimental effects on the cellular network’s traffic but also provides satisfactory performance for IoT users.

Effective path planning is crucial for mobile robots to navigate safely and efficiently in various environments. The main purpose of this study is to compare and investigate the performances of three important path planning algorithms, namely A*, Dijkstra and Rapidly exploring Random Trees (RRT), for a mobile autonomous robot with LiDAR sensors and mechanical wheels. The mobile robot can move in multiple directions with its mecanum wheels and can precisely avoid obstacles with the help of LiDAR sensors and decision-making mechanisms (path planning algorithms). Various scenarios with cluttered and open areas were used in a simulated ROS Gazebo environment to evaluate the effectiveness of each algorithm. The performance metrics among the algorithms were analyzed with respect to path length, time to reach the target, velocity command frequency and CPU utilization. In terms of travel time performance criterion, A* performed approximately 35% better than Dijkstra and 85% better than RRT. In terms of the path length traveled, A* reached the target in approximately 11% shorter path length than Dijkstra and 17% shorter path length than RRT. In terms of the number of velocity commands processed, A* outperformed Dijkstra by approximately 36% and RRT by 38%. In terms of CPU utilization performance criterion, RRT performed approximately 10% better than A* and 74% better than Dijkstra. As a result, significant information was obtained about the strengths and weaknesses of each algorithm in selecting the most appropriate path planning strategies for mobile autonomous robots.