Pneumonia, a leading cause of mortality among children and adults worldwide, necessitates timely and accurate diagnosis to reduce death rates. Deep learning models have emerged as a promising solution to automate pneumonia detection and improve healthcare efficiency. This study presents the first comprehensive analysis of the YOLOv11 Nano Classification Model (YOLOv11n) and the ResNet-50 model using a large, annotated chest radiograph dataset for pneumonia diagnosis. To the best of our knowledge, this research represents the first application of YOLOv11n to classification tasks in the context of pneumonia datasets.YOLOv11n demonstrated state-of-theart accuracy of 97.5%, significantly outperforming ResNet-50’s accuracy of 90.38%, marking a new benchmark for pneumonia detection. The analysis highlights the superior training dynamics, balanced metrics, and reduced false negatives of YOLOv11n, as validated through statistical evaluation. These findings underscore the model’s reliability for medical diagnostics, particularly in resource-constrained settings. By showcasing the potential of advanced machine learning algorithms like YOLOv11n, this research contributes to improving the accuracy and accessibility of pneumonia diagnosis in global healthcare.

With the commercialization of Fifth-Generation (5G) technologies, the emergence of data-intensive applications has driven the need for future-generation networks to support seamless, immersive and high bandwidth services while ensuring enhanced quality of service for mobile users. To address this, Millimeter Wave (mmWave) frequency bands offer substantial potential for delivering high data rates, though, prone to blockages due to clutter, particularly in dense urban environments. Thus, Integrated-Access-and-Backhaul (IAB) deployment enables network densification by improving the line-of-sight condition to the serving node, replacing part of the optical fibre backhaul links with mmWave wireless links. This paper evaluates the IAB-enhanced cellular network performance in a Manhattan-like urban setting, across four potential network deployment scenarios, comprising candidate locations for mobile User Equipment (UE) placement. The mobile UEs can connect to the serving node, either gNodeB (gNB) that may take the role of IAB donor or IAB node, enabling the best link performance. Simulation results demonstrate significant Signal-to-Interference-plus-Noise-Ratio (SINR) gains of 10dB at the 50th percentile across all IAB deployment scenarios when using the optimal path selection approach compared to forcing UEs to connect to a specific serving node, besides the best option. These SINR gains translate into improved spectral data rates, in the order of 0.11- 2.72 bps/Hz. This study emphasizes the strategic placement of IAB nodes and optimized path selection in dense urban mmWave networks to enhance the end-user experience.

The emergence of wearable medical devices and wireless body area networks (WBANs) has enabled continuous, real-time patient monitoring. These systems generate large volumes of health data, requiring low-latency and reliable processing for timely interventions. However, local processing is often inefficient due to the energy and computational limitations of mobile devices. Offloading tasks to edge computing and cloud resources offers a promising alternative. Nonetheless, optimizing offloading decisions in dynamic healthcare scenarios remains challenging due to heterogeneous task requirements and varying computational resources. This paper presents a hierarchical actor-critic task offloading approach (HACTO), a deep-reinforcement-learning framework designed to enhance the efficiency and adaptability of task offloading in healthcare scenarios. By introducing a hierarchical decision structure, HACTO reduces complexity and improves learning performance. The problem is modeled as a Markov decision process and solved using the deep deterministic policy gradient algorithm. HACTO jointly optimizes task offloading with respect to three objectives: meeting task deadlines, minimizing the energy consumption of mobile devices, and reducing resource usage costs. Our experimental results show that HACTO outperforms traditional and deep-reinforcement-learning-based offloading strategies, making it a promising solution for intelligent task offloading in resource-constrained WBAN environments.

Heterogeneous wireless networks (HetNets), where networks are deployed with ultra-dense small cells (SCs), is one of the main enabling technologies for future wireless networks. In such networks, signals are vulnerable to severe blockage, interference, and intermittent connectivity. This can be largely overcome using the emerging Reconfigurable Intelligent Surface (RIS) technology that can enhance HetNets performance by controlling the propagation environment. However, jointly optimizing the parameters of base stations’ (BSs’) active beamforming and RISs’ passive beamforming is a major challenge in RIS-empowered HetNets. In this paper, we investigate the issue of rate control in RIS-empowered multi-cell multiple-input single-output (MISO) HetNets via joint users’ equipment (UEs) rate fairness and SCs rate load balancing. We assume RIS-assisted SC BSs at mmWave underlying a RIS-assisted macrocell (MC) BS at sub-6GHz serving dual-connectivity UEs that can concurrently connect to the MC BS and a single SC BS. Then, we formulate an optimization problem whose objective is to jointly optimize the active transmit beamforming vectors of the MC and SCs BSs on the one hand and the passive beamforming vectors of the MC and SCs RISs on the other hand. Due to the high non-convexity and complexity of the formulated problem, we propose a novel distributed Deep Deterministic Policy Gradient (DDPG)-based multi-task deep reinforcement learning (MTDRL) scheme to solve the problem and learn network dynamics. Through deliberate definitions of MTDRL agent’s tasks and their corresponding main elements, we demonstrate via simulations that our proposed scheme guarantees a fair distribution of rates within UEs and SCs. In addition, we quantify the robustness of our proposed MTDRL scheme compared with some benchmarks in terms of convergence speed and utility values.

Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various commercial and military applications, such as object recognition, surveillance, and data acquisition. However, the broadcast communication nature of IoD networks raises significant communication security issues. This paper investigates drone-to-ground communication subject to eavesdroppers in urban environments. We aim to provide secure communication utilizing physical layer security by increasing network secrecy rates. In addition, we aim to reduce the energy consumption within the IoD network by optimizing drones’ transmitting and jamming power and employing energy harvesting techniques to charge drones wirelessly. Our optimization problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is proposed to solve the problem.

Reconfigurable Intelligent Surface (RIS) has recently emerged as an enabling technology to enhance reliability and overcome blockage in future heterogeneous wireless networks (HetNets). Adjusting amplitudes and phases of the RIS elements to achieve such goals is a major challenge. In this paper, we study the problem of network rate control to achieve users (UEs) fairness and smallcells (SCs) load balancing in multi-cell RIS-assisted multiple-input single-output (MISO) HetNets. We consider dual-connectivity UEs that can simultaneously connect to mmWave-operating SCs and sub-6GHz-operating RIS-assisted macrocell (MC), where RISs are mainly deployed to enhance sub-6GHz signal reception and mitigate interference. Then, we formulate an optimization problem whose objective is to jointly control the active beamforming vectors of SCs and MC on the one hand and the passive beamforming vectors of RISs on the other hand to maximize UEs fairness and network load balancing. Due to the high complexity of the formulated problem, we propose a novel multi-task deep reinforcement learning (MTDRL) model based on the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the problem and learn system dynamics. Through proper definitions of network tasks and their main elements, we show via simulations that our proposed MTDRL-based model ensures fair distribution of rates within UEs and SCs and that it outperforms key benchmarks.

In this paper, a Deep Q-Network (DQN) based multi-agent multi-user power allocation algorithm is proposed for hybrid networks composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multihoming, which can bridge RF and VLC links for accommodating their bandwidth requirements. By leveraging a non-cooperative multi-agent DQN algorithm, where each AP is an agent, an online power allocation strategy is developed to optimize the transmit power for providing users’ required data rate. Our simulation results demonstrate that DQN’s median convergence time training is 90% shorter than the Q-Learning (QL) based algorithm. The DQN-based algorithm converges to the desired user rate in half duration on average while converging with the rate of 96.1% compared to the QL-based algorithm’s convergence rate of 72.3%. Additionally, thanks to its continuous state-space definition, the DQN-based power allocation algorithm provides average user data rates closer to the target rates than the QL-based algorithm when it converges.

Internet of Things (IoT) is an innovative paradigm envisioned to provide massive applications that are now part of our daily lives. Millions of smart devices are deployed within complex networks to provide vibrant functionalities, including communications, monitoring, and controlling of critical infrastructures. However, this massive growth of IoT devices and the corresponding huge data traffic generated at the edge of the network created additional burdens on the state-of-the-art centralized cloud computing paradigm due to the bandwidth and resource scarcity. Hence, edge computing (EC) is emerging as an innovative strategy that brings data processing and storage near to the end users, leading to what is called the EC-assisted IoT. Although this paradigm provides unique features and enhanced Quality of Service (QoS), it also introduces huge risks in data security and privacy aspects. This article conducts a comprehensive survey on security and privacy issues in the context of EC-assisted IoT. In particular, we first present an overview of EC-assisted IoT, including definitions, applications, architecture, advantages, and challenges. Second, we define security and privacy in the context of EC-assisted IoT. Then, we extensively discuss the major classifications of attacks in EC-assisted IoT and provide possible solutions and countermeasures along with the related research efforts. After that, we further classify some security and privacy issues as discussed in the literature based on security services and based on security objectives and functions. Finally, several open challenges and future research directions for secure EC-assisted IoT paradigm are also extensively provided.

This paper presents propagation path loss channel models developed from real-field measurement campaigns that were conducted in indoor and outdoor Line-of-Site (LOS) propagation environments in Riyadh city, Saudi Arabia, using highly directional antennas at 60 GHz. The setup used in these measurements emulates the future fifth-generation (5G) cellular systems for both access and backhaul services, as well as for Machine-to-Machine (M2M) communications. We used our measurement data to develop the corresponding large-scale propagation path loss models at 60 GHz, using the log-distance and the floating intercept modeling approaches. It is shown that cellular radio links can be established in short-range distances up to 134 m indoors, and up to 77 m outdoors, when employing highly directional antennas at both the transmitter and receiver sides. It is also shown that path loss at 60 GHz in hot and sunny weather during the day, is higher than those obtained in cool and clear weather at night. This is partly due to solar radio noise effect arising from the intense solar radiation that characterizes summer afternoon in Riyadh city, which can cause a decrease in carrier-to-noise ratio at the input of receiving antennas. It is also partly due to the increase in thermal noise when electronics components in the measurement device become hot. The results presented here are thus very useful in 5G cellular design and infrastructure planning in the gulf region, where daytime temperature could reach 43° C or more.

Fifth-generation (5G) cellular systems are likely to operate in the centimeter-wave (3-30 GHz) and millimeter-wave (30-300 GHz) frequency bands, where a vast amount of underutilized bandwidth exists world-wide. To assist in the research and development of these emerging wireless systems, a myriad of measurement studies have been conducted to characterize path loss in urban environments at these frequencies. The standard theoretical free space (FS) and Stanford University Interim (SUI) empirical path loss models were recently modified to fit path loss models obtained from measurements performed at 28 GHz and 38 GHz, using simple correction factors. In this paper, we provide similar correction factors for models at 60 GHz and 73 GHz. By imparting slope correction factors on the FS and SUI path loss models to closely match the close-in (CI) free space reference distance path loss models, millimeter-wave path loss can be accurately estimated (with popular models) for 5G cellular planning at 60 GHz and 73 GHz. Additionally, new millimeter-wave beam combining path loss models are provided at 28 GHz and 73 GHz by considering the simultaneous combination of signals from multiple antenna pointing directions between the transmitter and receiver that result in the strongest received power. Such directional channel models are important for future adaptive array systems at millimeter-wave frequencies.