The transition from 5G to 6G networks is driving an unprecedented densification of Radio Access Networks (RANs), resulting in increased energy demand and operational costs. Open RAN (O-RAN) architectures, which disaggregate baseband processing across commercial off-the-shelf (COTS) servers, further amplify these challenges due to the power sensitivity of the Distributed Unit (O-DU) to traffic load variations. This paper develops a nonlinear O-DU power model that captures load-dependent efficiency effects and integrates it into a dynamic core management framework, which activates or deactivates CPU cores based on instantaneous traffic demand. The effectiveness of the proposed dynamic core management framework is investigated in multi-RU O-RAN scenarios, comparing the proposed dynamic approach with a static baseline. Simulation results show that the dynamic scheme reduces energy consumption by up to 59.42% on average under light traffic, with instantaneous savings exceeding 70% during off-peak periods, while maintaining performance. These findings demonstrate that workload-aware core provisioning can deliver substantial energy efficiency gains in O-RAN deployments, particularly in lightly to moderately loaded networks.

The rapid adoption of the Internet of Things (IoT) across various industries has significantly enhanced productivity and efficiency. However, the widespread deployment of battery-powered IoT devices raises concerns about energy sustainability, battery longevity, and environmental impact. Frequent battery replacements pose logistical challenges, particularly in large-scale networks and remote locations. To address these issues, the Green IoT (G-IoT) design framework has been introduced, emphasizing energy-efficient strategies such as duty cycling, transceiver optimization, energy-aware routing, and the integration of renewable energy sources like photovoltaic (PV) energy harvesting. Despite advancements in G-IoT, a major challenge remains: the dynamic and unpredictable nature of both IoT energy consumption and harvested energy. In this paper, we propose a design framework for Green IoT that models the interplay between energy harvesting, storage, and consumption processes. By incorporating time-varying energy dynamics, the proposed framework enables efficient dimensioning of energy harvesters and storage systems, ensuring optimal energy management in IoT networks. This approach allows for the evaluation of various energy-saving strategies and enhances the long-term sustainability of IoT deployments. Numerical examples and simulation results demonstrate the effectiveness of the framework in maintaining energy balance and extending the operational lifetime of IoT nodes.

This paper details the algorithm implementation of the Switching Sequence Table method in asynchronous sequential circuit Boolean switching system design. The algorithm is theoretically analysed in terms of its computational complexity and validated through actual experiments. It discusses the multidimensional problem of various parameters and their impact on performance. It then presents analytical evidence that reducing this multidimensional problem to a standard, one-dimensional problem is feasible. The authors consider method challenges when implementing computing modules for the Switching Sequence Table method.

The rapid adoption of the Internet of Things (IoT) across various industries, including manufacturing, healthcare, transportation, energy, and smart cities, has created a significant demand for skilled professionals in IoT hardware design, software development, deployment, and maintenance. However, the educational landscape has struggled to keep pace with this demand, as many universities lack dedicated IoT programs or fail to integrate IoT education into non-IT disciplines. This gap has led to a shortage of professionals skilled in IoT, posing a challenge for industries seeking to harness IoT technologies effectively. To address this challenge, next-generation remote laboratory infrastructures have emerged as a viable solution, enabling hands-on learning with IoT devices without the constraints of physical access to hardware. These infrastructures are particularly valuable for lifelong learners, students in remote areas, and institutions with limited resources, offering scalable and flexible access to IoT experimentation platforms via the Internet. Furthermore, the COVID-19 pandemic underscored the necessity of resilient and accessible online learning solutions, reinforcing the role of Virtual and Remote E-Laboratory (VREL) platforms in modern education. This paper presents the design, implementation, and operation of a next-generation remote laboratory infrastructure for IoT and Industry 4.0 education and research. Our approach builds upon the outcomes of two European Commission-funded projects—IOT-OPEN.EU (2016-2019) and IOT-OPEN.EU Reloaded (2022-2025) focuses on providing scalable, secure, and accessible remote IoT education solutions. We outline the architectural framework, key components, and technical challenges of deploying such infrastructures, including connectivity, security, scalability, and user management. Additionally, we discuss the educational impact of these platforms and their role in addressing the global IoT skills gap. Our findings demonstrate that remote IoT laboratories can be an effective and scalable alternative to traditional hands-on learning, supporting academic institutions and industry professionals in acquiring practical IoT skills. These infrastructures can enhance IoT education and workforce readiness by leveraging open-source technologies and modular architectures, ensuring broader accessibility and fostering innovation in the field.

The rapid deployment of 5 G and the anticipated evolution to 6 G have intensified the need for energy-efficient radio access networks, driven by rapidly growing power demands, rising energy costs, and environmental concerns. Open Radio Access Networks (O-RAN) offer a flexible and scalable architecture; however, their disaggregated nature requires accurate power modelling to optimise energy consumption. This paper proposes enhanced power consumption models for O-RAN that account for power efficiency gains from resource sharing across multiple radio units (O-RUs) and the offloading of highly parallel baseband processing tasks to specialised energy-efficient hardware accelerators such as ASICs, FPGAs, or GPUs. The models are used to analyse the scaling behavior of O-RUs, distributed units (O-DUs), and centralized units (O-CUs) under varying deployment scenarios. Results show that O-RUs dominate the total network power budget, while O-DUs benefit from sublinear growth due to statistical multiplexing, and O-CUs scale the slowest owing to centralised control-plane sharing. Furthermore, the study identifies optimal ranges of RU-to-DU aggregation and accelerator utilization for maximising energy efficiency in largescale $\mathbf{5 G} / \mathbf{6 G}$ O-RAN deployments.

The growing proliferation of Internet of Things (IoT) devices has intensified the need for sustainable energy solutions, particularly in resource-constrained deployments where non-rechargeable batteries and supercapacitors are the primary energy sources. Green IoT (G-IoT) frameworks address this challenge by combining energy-saving techniques with energy harvesting from ambient sources such as solar power. However, the intermittent nature of renewable energy and the non-ideal behavior of energy storage systems-such as energy leakage and capacity degradation-complicate reliable energy provisioning. This paper presents a novel Markovian framework for modelling the coupled dynamics of time-varying solar energy harvesting, time-dependent energy consumption, and state-dependent energy leakage in G-IoT systems. Unlike traditional steady-state models, our approach uses Discrete-Time Markov Chains (DTMCs) to capture the stochastic variability in both energy harvesting and consumption processes. We also introduce a refined leakage model in which the leakage rate is dynamically dependent on the stored energy level, enabling a more realistic characterization of energy losses due to energy leakage. Through extensive analytical evaluation, we examine how key parameters-such as storage capacity, leakage rate coefficient, and energy harvesting and consumption patterns-affect critical performance metrics, including the mean stored energy and energy-related service outage probability. Furthermore, we propose a parameter tuning strategy to optimize energy reliability and storage efficiency. The proposed model provides valuable insights for the design and optimization of robust, energy-aware IoT systems powered by renewable energy sources.

The growing demands of$\mathbf{5 G}$and emerging$\mathbf{6 G}$mobile networks-driven by the proliferation of IoT devices, immersive applications, and high-throughput services-necessitate more efficient and scalable fronthaul solutions. Open Radio Access Networks and cloud-RAN architectures offer promising frameworks by decoupling hardware and software and enabling centralised processing. However, the fronthaul segment still faces challenges related to bandwidth inefficiency, signalling overhead, and relatively high energy consumption, particularly due to the transmission of vast numbers of small packets. Packet aggregation emerges as a viable strategy to mitigate these challenges by grouping multiple small packets into larger payloads, thereby reducing protocol overhead, improving throughput efficiency, and lowering energy consumption in the network as a whole. This paper investigates the impact of packet aggregation on both quality of service (QoS) and energy efficiency in the fronthaul of$\mathbf{5 G} / \mathbf{6 G}$mobile networks. While prior studies have examined its effects on throughput and latency, the implications for energy consumption remain underexplored. We evaluate how aggregation design parameters-such as time thresholds, size thresholds, and traffic intensity-influence performance metrics such as throughput and latency. We also propose ways to achieve an optimal trade-off between QoS and energy efficiency. Our findings offer insights into the design of energy-aware, delay-sensitive packet aggregation schemes tailored for next-generation mobile network architectures.

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming.

Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (primarily water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery’s energy content and to estimate the mean and variance of the device’s lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node’s lifetime.

In the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).

There is a growing awareness of the need to reduce carbon emissions from the operation of mobile networks. The massive deployment of ultra-dense 5G and IoT networks will significantly increase energy demand and put the electricity grid under stress while also driving up operational costs. In this paper, we model the energy performance of an off-grid sustainable green cellular base station site which consists of a solar power system, Battery Energy Storage (BESS) and Hydrogen Energy Storage (HESS) system, and various types of macrocells, microcells, picocells, or femtocells, with broadband optical or microwave transmission systems, and other electrical and electronic systems (air conditioner, power converters, and controllers. We propose diffusion-based models of the charging and discharging processes of the energy storage systems, and obtain the probability of charging them to their full capacities during the day and completely discharging them at the end of each day. We also investigate the impact of design parameters such as the mean charging rate and the mean discharging rate on the probability densities of charging BESS and HESS to their full capacities during the day and of completely discharging them before the end of each night period.

The complexity of battery-powered autonomous devices such as Internet of Things (IoT) nodes or Unmanned Aerial Vehicles (UAV) and the necessity to ensure an acceptable quality of service, reliability, and security, have significantly increased their energy demand. In this paper, we discuss using a diffusion approximation process to approximate the dynamic changes in the energy content of a battery. We consider the case when energy harvesting sources are constantly charging the battery. The model assumes a probabilistic consumption and delivery of energy, giving the time-dependent distributions of the energy at the battery, of the time remaining until it becomes empty, the time required to charge the battery to its total capacity, or the time it is operational between two moments of complete depletion. When possible, we compare the diffusion approximation results with corresponding models based on continuous-time Markov chains.

In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the coronavirus that is causing the disease. As a result, some universities and schools have switched from physical classrooms to virtual or online classrooms. This approach is working well for theoretical subjects and courses, but it is not straight forward in the case of laboratory subjects and courses that require access to hardware resources. The IOT-OPEN.EU remote laboratory infrastructure presented in this paper is a timely solution. In this paper, we present current advances in distant learning, distant laboratory models, and the IOT-OPEN.EU remote laboratory implemented as part of the IOT-OPEN.EU ERASMUS+ project, along with short analysis on current advances in distant learning, where students are interacting with physical hardware remote way.