The capacity and delay of wireless multi-hop networks are key performance indicators. They are needed in the design of such networks, and are also useful to assess how well applications can run over such networks. However, finding analytical expressions for capacity and delay for a wireless multi-hop network with an arbitrary topology is hard. In this work, a two-step approach is followed to derive expressions for the maximum achievable capacity and the minimum achievable delay in wireless multi-hop networks. These expressions are valid for fixed, conflict-free time-slot scheduling among the nodes, and when each nodes of the network has always packets to send to each of its neighbours. In the first step, expressions for the capacity and delay of the elementary topologies ’string’ and ’star’ are derived, and in the second step, these results are combined to derive the capacity and delay values for a network with an arbitrary topology. This two-step approach is applied to two types of wireless multi-hop networks: those whose nodes have an omni-directional antenna, and those whose nodes have an electronically steerable directional (beam-steering) antenna. Using this approach, we find that the capacity of a path on the network is not a decreasing function of the total number of nodes in the network, as mostly found in literature, but rather a decreasing function of the number of neighbours of the nodes on the path. The results show that the derived maximum value for the capacity (and minimum value for the delay) in networks with beam-steering antennas is larger (lower) than that for networks with omni-directional antennas as more efficient scheduling is possible for the former. The derived analytic expressions for capacity and delay are valuable for the relative comparison of the performance of wireless multi-hop networks.

Industrial IoT networks are characterized by diverse use cases, each with distinct Quality of Service (QoS) requirements. Ensuring these requirements is critical for the successful deployment of applications and maintaining operational efficiency in industrial environments. Furthermore, IoT sensors and devices often operate under strict energy constraints, necessitating energy-efficient solutions. Consequently, effective network management must simultaneously optimize both energy and spectrum efficiency. To overcome this challenge, we propose a deep reinforcement learning (DRL) framework that dynamically optimizes the Modulation and Coding Scheme (MCS) and transmission power in WiFi-based IIoT networks. Our results demonstrate that the DRL-based approach can effectively enhance both the energy and spectrum efficiency. Our findings also reveal that aggressive energy-saving strategies can compromise spectrum efficiency and QoS, underscoring the need for careful trade-offs in network optimization.

In recent years, Radio Frequency Spatial Networks (RF-SNs) have struggled to meet the increasing demand for higher data rates due to their limited bandwidth. To address this challenge, Free Space Optical Spatial Networks (FSO-SNs) have been proposed as a promising solution that aims to fulfill the stringent requirements for high data rates, enhanced security, and global coverage. Despite their potential, FSO-SNs face significant challenges due to their dynamic nature caused by the mobility of spatial nodes and fluctuating weather conditions, leading to frequent topology changes and inefficient routing. However, in RF-SNs, the dynamicity is mainly attributed to node mobility, since Radio Frequency (RF) is more robust to weather impairments. Consequently, the topology changes in RF-SNs are less frequent and do not pose routing issues as in FSO-SNs. This paper presents a comparative analysis of the dynamicity in RF-SNs and FSO-SNs, providing valuable insight into the rate of topology change. The analysis reveals that, on average, topology changes occur every 43.6 seconds in FSO-SNs, with a total of 1981 topology changes during a period of 24 hours, 61.4% of which occur within 41 seconds after the previous change. In contrast, RF-SNs experience only 441 topology changes, with only 20.6% of these occurring before 41 seconds after the previous change. Furthermore, we analyze the impact of weather conditions on the maximum achievable data rates for both FSO-SN and RF-SN technologies, observing drastic fluctuations in Free Space Optical (FSO) systems. This study provides a solid foundation for developing tailored routing solutions for FSO-SNs.

Unmanned vehicles (UVs), including aerial (UAVs), ground (UGVs), and sea-based (USVs) systems, are advancing rapidly, where the combined market is projected to go from over $29.3 billion in 2025 to more than $46 billion by 2030 [1]. Despite the market growth, the development occurs in isolated silos. However, real-world missions increasingly demand coordinated operations across multiple UV domains. In this paper, we argue the need for a unified approach to UV development and propose a Synergistic UxV Ecosystem architecture to enable heterogeneous UVs to share information, collaborate, and dynamically allocate tasks. The proposed high-level conceptual framework integrates collaborative intelligence, decentralized communication, and service-oriented design to improve mission effectiveness. We demonstrate how such an ecosystem can enhance situational awareness, adaptability, and operational efficiency, enabling unmanned systems to achieve what isolated entities cannot, through real world inspired scenarios as examples.

Multi-Agent Reinforcement Learning (MARL) enables complex drone swarm coordination; however, the mission success is hindered by unreliable communication. Existing corrective solutions often require integration during training or significant configuration, limiting flexibility. This paper introduces a Trust-Based Information Filtering (TIF) system that enhances pre-trained MARL policies during decentralized execution. The post-hoc TIF system equips each agent with a mechanism to assess message trustworthiness using learned spatiotemporal expectations from normal operations. This dynamic self-configuration eliminates the need for attack data or policy retraining. Evaluated in UAV formation control under various communication unreliability scenarios, TIF demonstrates a measurable improvement in operational resilience. This validates the prototype of effective, lightweight, post-hoc filtering approach, signaling that robustness can be layered onto existing MARL policies without costly retraining.

With the emergence of advanced use cases in In-dustrial, Healthcare and Smart City loT networks, applications and devices exhibit a wide range of QoS requirements. In the realm of loT, WiFi is a widely adopted technology that employs Enhanced Distributed Channel Access (EDCA) for QoS provisioning. However, EDCA's reliance on just four Access Categories (ACs) falls short in meeting the diverse range of QoS requirements of loT networks. SG-EmPOWER, a Software Defined Networking (SDN) framework, has introduced Network Slicing to WiFi networks, offering a robust solution to address the QoS diversity challenges in loT networks. SG-EmPOWER ensures slice priorities through quantum assignments to the slices, however this approach faces limitations in achieving effective slice differentiation, particularly in light of WiFi's Distributed Coordination Function (DCF). To overcome this limitation, we have introduced channel access parameter such as Contention Window (CW) into the slice configuration in addition to quantum value. This not only improves slice QoS differentiation but also brings robust and dynamic control over network QoS. Our proposed slice control mechanism has demonstrated substantial improvements, with an average increase of 47.66 % in per-slice throughput and an average reduction in jitter in high-priority slices of 23 %.

In this paper, a methodology for developing a human walking model adapted to the individual measured by radar was proposed. The fundamental parameters used in the model are the gait parameters and physical dimensions. Empirical validation of the proposed methodology is undertaken, involving the acquisition of data using a 77-GHz FMCW radar. The data is collected from three distinct individuals walking five different trajectories with respect to the radar. Moreover, the gait parameter estimation accuracy is evaluated for the different walking trajectories of the target. The studied gait parameters are speed, step length, and step frequency. These could be estimated with mean errors up to 0.077 m/s, 9.3 cm, and 0.128 Hz for all trajectories, respectively. Nevertheless, these errors diminish to 0.022 m/s, 2.2 cm and 0.03 Hz, respectively when the targets walk in a straight trajectory aligned with the radar beam. Moreover, the feasibility of estimating body part dimensions directly from the radar data is investigated. It was found that only the total human height could be directly estimated using the employed hardware. Except for the tallest participant of 2.01 m, the height could be estimated with a mean absolute error up to 10.9 cm. Enhanced hardware configurations or the integration of machine learning techniques may improve the accuracy of body part dimension estimations.

Rising demand for Quality of Service (QoS) guarantees in wireless networks, driven by new use cases and technologies, necessitates innovative solutions to efficiently manage resources and meet diverse requirements. Traditional approaches addressing single-layer parameters provide limited gains, as cross-layer parameters significantly influence QoS metrics like throughput, latency, and packet loss. Employing a structure learning approach via mutual information and entropy concepts, we uncover interrelationships between layer parameters and their impact on QoS metrics. Our study demonstrates how parameters like Contention Window, Transmit Power, Queue Size, ACK Timeout, and MCS values impact network performance in WiFi-based IoT networks. Based on our findings, we have proposed an architecture that can exploit these cross layer interactions and relationships in a SDN controlled network to develop an AI based improved QoS management solution.

The article evaluates the effectiveness of coarsegrained channel quality prediction (CQP) for 5G-RedCap/5G NR-Light devices within industrial IoT (IIoT) networks. Finegrained predictions refine real-time communication, enhancing throughput and reducing resource utilization (RU), albeit with increased computation complexity. In contrast, coarse-grained CQP offers low computational overhead while optimizing longterm network characteristics, such as redundancy planning. Our study investigates the potential applications of coarsegrained CQP in real-time communication within the IIoT context, aiming to enhance the efficiency of simple devices (5G-RedCap) without adding the computational overhead. The varying traffic profiles and quality of service levels across diverse 5G use cases, including massive Machine Type Communication (mMTC), enhanced Mobile Broadband (eMBB), and Ultra-Reliable and Low Latency Communication (URLLC), present different challenges. For mMTC devices, coarse-grained CQP demonstrates comparable RU gains to fine-grained CQP (with up to a 50% reduction in RU), showcasing its effectiveness without added complexity. However, in the eMBB scenario, where throughput is paramount, it yields only marginal improvements. Similarly, RU gains for URLLC devices are negligible due to their stricter QoS requirements. The effectiveness of coarse-grained CQP is intricately linked to the variability in experienced channel quality across different scenarios within an indoor IIoT network. This research underscores the potential of AI applications for enhancing the performance of simple 5G-RedCap/5G NR-Light devices without compromising device complexity.

Recent advancements in non-invasive health mon-itoring technologies underscore the potential of mm-WaveFrequency-Modulated Continuous Wave (FMCW) radar in real-time vital sign detection. This paper introduces a novel dataset,the first of its kind, derived from mm-Wave FMCW radar,meticulously capturing heart rate and respiratory rate undervarious conditions. Comprising data from ten participants, in-cluding scenarios with elevated heart rates and participants withdiverse physiological profiles such as asthma and meditationpractitioners, this dataset is validated against the Polar H10sensor, ensuring its reliability for scientific research. This datasetcan offer a significant resource for developing and testingalgorithms aimed at non-invasive health monitoring, promising tofacilitate advancements in remote health monitoring technologies.

Recognizing animal activities holds a crucial role in monitoring animals' health and well-being. Additionally, a considerable audience is keen on monitoring their pets' well-being and health status. Insight into animals' habitual activities and patterns not only aids veterinarians in accurate diagnoses but also offers pet owners early alerts. Traditional methods of tracking animal behavior involve wearable sensors like IMU sensors, collars, or cameras. Nevertheless, concerns, including privacy, robustness, and animal discomfort persist. In this study, radar technology, a noninvasive remote sensing technology widely employed in human health monitoring, is explored for AAR. Radar enables fine motion analysis through Microdoppler spectrograms. Utilizing an off-the-shelf FMCW mm-wave radar, we gather data from five distinct activities and postures. Merging radar technology with Machine Learning and Deep Learning algorithms helps distinguish diverse pet activities and postures. Specific challenges in AAR, such as random movements, being uncontrollable, noise, and small animal size, make radar adoption for animal monitoring complex. In this study, RayPet unveils different challenges and solutions regarding monitoring small animals. To overcome the challenges, different signal processing steps are devised and implemented, tailored for animals. We use four types of classifiers and achieve an accuracy rate of 89 This progress marks an important step in using radar technology to observe and comprehend activities and postures in pets in particular and in animals in general, contributing to our knowledge of animal well-being and behavior analysis.

This study presents a non-invasive method using thermal imaging to estimate heart and respiration rates in calves, avoiding the stress from wearables. Using Kernelised Correlation Filters (KCF) for movement tracking and advanced signal processing, we targeted one ROI for respiration and four for heart rate based on their thermal correlation. Achieving Mean Absolute Percentage Errors (MAPE) of 3.08 of thermal imaging in vital signs monitoring, offering a practical, less intrusive tool for Precision Livestock Farming (PLF), improving animal welfare and management.

WiFi, a widely used technology in IoT, provides QoS through IEEE 802.11e EDCA Access Categories (AC) which have fixed configuration with strict priorities. This makes WiFi QoS unsuitable to the rising QoS diversity, changing wireless conditions and network dynamics. QoS slicing, where network resources are divided into chinks for diverse QoS requirements, is a potent technology that can provide more flexible, adaptable and highly configurable QoS in $W$ iFi based IoT networks. However, resource management of QoS slices with diverse IoT applications, limited network capacity and varying channel conditions is a complex and challenging task. Traditional queuing theoretic and optimization models become intractable to solve such complex and dynamic problem. Therefore, we have developed a Deep Reinforcement Learning (DRL) based slice resource management scheme to meet IoT QoS requirements in a real world 5GEmpower controlled SDN network. Our proposed scheme outperforms Airtime Excess Round Robin (ATERR) scheme and no slicing-based scheme in terms of slice throughput (QoS) satisfaction. Moreover, our proposed scheme is tested in real world environment and can adapt to the changing slice requirements in an IoT network.

The use of unmanned aerial vehicles (UAVs) is gaining significant interest for a variety of emerging applications. One of the most attractive use cases for network operators is the ability of UAVs to carry base station equipment on board. The usage of UAVs as moving base stations may enhance connectivity quality in congested areas and expand coverage. Particularly useful for millimeter-wave (mmWave) 5G New Radio (NR) networks, UAV base stations (UAV-BSs) operating at higher altitudes may benefit from broader coverage, improved line-of-sight conditions, and reduced blockage. This paper evaluates UAV-aided radio systems by employing system-level simulations and compares them with traditional terrestrial networks, where urban deployment is modeled using real-world data. Our results show improvements in the average user throughput when using UAVs as flying base stations in urban environments. On top of this, we provide an updated review of 3GPP activities related to UAVs and NR-based relaying.

The increasing demand for providing an animal-friendly environment that ensures animal well-being and economic efficiency in animal farming leads to more advanced and efficient animal farming methods. Precision livestock farming (PLF) can simultaneously remove time-consuming tasks and extra labor for a farmer and improve animal well-being and welfare. In this review, we describe the PLF concept with a focus on recently born pigs (piglets) which require extra care and attention due to their vulnerability and sensibility. To do so, we identify the top five mortality causes in newborn piglets: low vitality, hypothermia, starvation, crushing, and hypoxia. Furthermore, the correlation and interconnections among these mortality causes are explored. Then, the most relevant health indicators (HIs), which can be early indicators of an animal issues, are also introduced. Each mortality cause can be recognised through some symptoms, which can then be used as indicators. Considering these indicators, we can improve the animals' well-being and avoid unforeseen losses. Novel sensing and communication technologies in PLF can be utilized to monitor these HIs to give an early warning to the farmer in case something is wrong. Different novel sensing and communication technologies, whether tested on animals or not, are investigated in this review to give the researcher a better idea of available technologies that can be used in PLF implementation.

In this paper, we propose VarOLLA, a novel approach aimed at maximizing the throughput of 5G-RedCap devices in Industrial Internet of Things (IIoT) environments. VarOLLA addresses the sub-optimal spectral efficiency of Outer Loop Link Adaptation (OLLA) by introducing a ‘Throughput Factor’ that incentivizes adaptive decision-making based on throughput considerations. Through extensive simulations, we demonstrate significant improvements in throughput, with gains of up to 35% compared to traditional OLLA techniques, particularly in scenarios with high channel outdatedness. Moreover, VarOLLA effectively reduces consecutive transmission failures (rBLER) and achieves substantial reductions in control message overhead, up to 87.5%. Our findings highlight the strong potential of VarOLLA in IIoT networks and its significant contribution to the realization of high-performance applications during the Fourth Industrial Revolution (4IR) in the manufacturing sector.

The diversity of QoS requirements in modern IoT networks is expanding thus requiring more flexible and autonomous solutions. Network Slicing is an efficient technology that can enable efficient and flexible management of QoS in IoT networks by dividing network resources into small chinks and assigning them to the slices. However, proposed solutions in literature require predefined slices which require prior knowledge of network traffic thus limiting application of these solutions. To address this limitation, we have developed a dynamic and autonomous network slicing solution in a WiFi based IoT network using 5GEmpower SDN controller that creates slices depending on user traffic flows and priorities. Moreover, it autonomously estimates slice throughput requirements to subsequently allocate network resources to meet their QoS requirements. Our system is able to differentiate between multiple QoS requirements and provides framework for flexible prioritization policies for better QoS in IoT networks.

The deployment of millimeter-wave (mmWave) 5G New Radio (NR) networks is hampered by the properties of the mmWave band, such as severe signal attenuation and dynamic link blockage, which together limit the cell range. To provide a cost-efficient and flexible solution for network densification, 3GPP has recently proposed integrated access and backhaul (IAB) technology. As an alternative approach to terrestrial deployments, the utilization of unmanned aerial vehicles (UAVs) as IAB-nodes may provide additional flexibility for topology configuration. The aims of this study are to (i) propose efficient optimization methods for airborne and conventional IAB systems and (ii) numerically quantify and compare their optimized performance. First, by assuming fixed locations of IAB-nodes, we formulate and solve the joint path selection and resource allocation problem as a network flow problem. Then, to better benefit from the utilization of UAVs, we relax this constraint for the airborne IAB system. To efficiently optimize the performance for this case, we propose to leverage deep reinforcement learning (DRL) method for specifying airborne IAB-node locations. Our numerical results show that the capacity gains of airborne IAB systems are notable even in non-optimized conditions but can be improved by up to 30 % under joint path selection and resource allocation and, even further, when considering aerial IAB-node locations as an additional optimization criterion.

As IoT networks become larger and more diversified, service discovery becomes a cumbersome task. Nodes need to be polled and the upkeep of routing tables might not be possible in low-power devices as this comes with a considerable energy cost and added complexity. For these reasons, flooding is a very well-known strategy for building large peer networks with low-energy protocols. But flooding requires blindly spreading information until the destinations have been reached and is poorly suited to unicast traffic. In this paper we explore a fully distributed topology discovery in low-energy networks, we show that flooding can be a great strategy for early communication while a topology is built. We investigate which additional information should be added to a data packet in order to build the topology quickly and reliably and what is the trade-off in terms of communication overhead. Furthermore, we show that not all nodes need to be queried for a reliable topology discovery but traditional centrality measures can be exploited and specific nodes can be targeted based on the partial topology each node is able to build over time.

This work proposes compact multimode Multiple-Input–Multiple-Output (MIMO) antennas for Angle of Arrival (AoA) estimation in miniaturized Internet of Things (IoT) systems. The method excites different orthogonal radiating modes (TM $_{21}$ , TM $_{02}$ , and TM $_{31}$ modes) for beamforming capabilities, and the AoA performance is investigated using the Multiple Signal Classification (MUSIC) algorithm, executed using numerical and experimental data. The technique is tested at $2.238 \,\mathrm{GHz}$ , while using an antenna diameter $< 7.5 \,\mathrm{cm}$ . Overall, an envelope correlation coefficient $< 0.01$ is realized between all antenna ports, and a $360^{\circ }$ field of view AoA estimation is demonstrated across the entire azimuthal plane with mean absolute errors smaller than $0.098^{\circ }$ . Furthermore, it is shown that the capability of separating multiple simultaneous impinging signals is comparable to that of state-of-the-art circular arrays with similar number of RF transceivers, while offering, respectively, up to 35% and 71% antenna diameter and height miniaturization.