ABSTRACT Smart healthcare systems are expected to grow exponentially in the 6G era, relying on their capacity to provide superior network services in terms of data rates and deployment scale. This generates vast amounts of health data that must be processed in real‐time. Therefore, computational offloading for smart healthcare systems is an inevitable trend and is widely applied in numerous medical applications, ranging from image diagnosis and clinical treatment to responding to viral pandemics. In this study, we provide a comprehensive overview of computational offloading for Health Internet of Things (HIoT) applications, examining various aspects. Then, we propose a deep reinforcement learning (DRL)‐based intelligent task offloading framework that performs decision‐making on local devices, accounts for real‐time resource constraints and provides detailed analyses. The results demonstrate that the DRL‐based offloading strategy outperformed Greedy and Threshold‐based methods by 20%, 10% and 45% in latency, energy consumption and failure rate, respectively. Finally, we identified challenges and open issues related to pervasive smart healthcare systems.

ABSTRACT Unmanned Aerial Vehicle (UAV) networks are increasingly deployed in dynamic environments where reliable and low‐latency communication is critical. However, high mobility, intermittent connectivity, spectrum limitations, and energy constraints make conventional static communication protocols inadequate for maintaining stable, dependable links. To address these challenges, this paper proposes TRC‐MAPPO, a topology‐aware reliability‐constrained multi‐agent deep reinforcement learning framework for adaptive UAV swarm communication. The routing problem is formulated as a constrained decision‐making task that jointly considers packet delivery reliability, end‐to‐end delay, link stability, bandwidth usage, and energy consumption. Unlike single‐agent DRL baselines, TRC‐MAPPO represents the swarm as a dynamic communication graph and combines local relay selection with centralized training, enabling cooperative routing decisions under time‐varying network conditions. Simulations are conducted in a controlled, dynamic UAV environment, with the same mobility and traffic settings used for all methods. The proposed framework is compared with DQN and PPO over different swarm densities. Results show that TRC‐MAPPO achieves a higher packet delivery ratio, lower end‐to‐end delay, and more efficient energy behaviour, particularly in dense deployments. These findings indicate that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks.

ABSTRACT The sixth‐generation communications networks (6G) are expected to be deployed in the 2030s with integrated space‐aerial‐ground and undersea architecture to provide seamless global connectivity. In this architecture, unmanned aerial vehicles (UAVs) are one of the most unique characteristics and are becoming increasingly important. The flexibility, high speed, and infrastructure independence of UAV systems make them ideal for many applications. However, these advantages also create great challenges in effective communication between UAVs. To address these challenges, reinforcement learning (RL) algorithms such as Q‐Learning have been investigated. However, the traditional Q‐learning algorithm mainly relies on delay parameters in the reward function for decision‐making route selection. Aiming to optimise the selection of sustainable and efficient communication for UAVs, we propose an improved routing algorithm based on Q‐Learning for UAV communication. Our method integrates latency, energy consumption, and link quality parameters into the reward function to make smarter routing decisions. The simulation results show that Q‐Proposed achieves significant gains in terms of packet delivery ratio and end‐to‐end delay compared to other methods, paving the way for sustainable 6G UAV communications.

Human Activity Recognition (HAR) has become a cornerstone in the dynamic development of the Industrial Internet of Things (IIoT). This study introduces an extensive framework aimed at embedding HAR functionality into industrial settings to promote workplace safety, streamline operations, and facilitate predictive maintenance. Through AI techniques, HAR problems can be achieved with high accuracy. However, traditional AI models require centrally trained data on remotely powerful cloud servers. This leads to issues with privacy and security of health records and increases latency. To address this problem, the Federated Learning (FL) technique was proposed. FL allows distributed training on the patient’s IoT devices and serves as the communication mechanism between local devices and the FL aggregator. Thanks to this architecture, the health data needs only to be stored locally on its devices without being uploaded to data centers, thus ensuring security and reducing service response times and computational costs. In this study, we implement FedANN and FedConvNN independently in a federated learning setting to address human activity recognition problems toward real-time applications. Finally, we evaluate the effectiveness of the based on the variation initiation of the number of different training clients. The results show that the FedConvNN solution improves accuracy and reduces model and communication complexity compared to FedANN and centralized training models, with the potential for real-time deployment in HAR tasks. Our code is available on our GitHub repository: https://github.com/itsminhcs/Fedavg-HAR.git.

The development of the healthcare industry is closely tied to the history of human development. The integration of sensing, communication, computing, and control technologies, along with cloud-based solutions, enables the realization of the Internet of Things concept and forms a series of Internet of Healthcare Things (IoHT) applications. However, providing realtime health services is one of the most important challenges for IoHT systems. To address this issue, a fog computing architecture (FC) is proposed as an additional computing layer to support the cloud computing layer, aiming to reduce service response time, computing costs, and energy consumption. In this study, we conduct a comprehensive evaluation to optimize resource allocation for hospitals under the constraints of scale and patient volume, as well as SLA thresholds. Then, we provide recommendations to optimize investment costs for computing infrastructure supporting real-time health services. Finally, we discuss challenges and open issues.

Sports and fitness activities play a vital role in modern society by reducing stress and enhancing overall health. With advancements in semiconductor technologies and communication systems driven by the Internet of Things (IoT), wearable devices have become increasingly compact, capable, and sensor-rich. These devices support monitoring vital signs and track physical activities in real time. However, wearables face significant challenges due to limited computational capacity, low power, and platform heterogeneity, especially when handling data-intensive, real-time processing. To address these issues, this study introduces an efficient task-offloading framework for fitness-oriented wearable devices. The proposed approach employs a convex optimization algorithm with multiple constraints, dynamically adapting to network conditions to optimize offloading decisions. Experimental results demonstrate that the framework enhances execution speed by 15.8% and 7.9%, and reduces energy consumption by 17.6% and 8.8%, compared to local and cloud-based execution methods, respectively.

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the Software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems’ service response times across various workloads and traffic.

The 6th generation mobile networks (6G) are expected to be launched in the 2030s. The architecture of 6G will be the integration of heterogeneous mobile networks. One of the indispensable components of 6G is unmanned aerial vehicle (UAV) communication networks. Powerful support capabilities with high mobility and flexibility are the main driving forces behind the breakout of UAV networks into countless domains to enhance the quality of human life such as disaster rescue, agriculture, military, and smart cities. However, high mobility and real-time services also pose significant challenges for communication protocols. In this work, we consider in detail the requirements of the modern 6G UAV communications and then develop a more realistic simulation environment based on NS3, to evaluate the performance of traditional routing protocols consisting of AODV, DSR, and OLSR on aspects of delay, packet delivery ratio, and routing overhead. We hope that these quantitative results will be an important guide for selecting suitable routing protocols in different 6G UAV scenarios.

The development of medicine hand-in-hand with the history of humans. The advent of 5th-generation communication networks have realized the Internet of Things concept and formed a series of smart applications in almost domains such as health-care, agriculture, transportation, retails, etc. In these contexts, the Internet of Medical Things (IoMT) is one of the most attended domains, where the service response time is a key design factor. In this study, we consider the effectiveness of this framework and compare it with the cloud-based computing framework under varying changes in the arrival rate of service requests by queuing models. The simulation results have demonstrated that the proposed fog-to-cloud based computing scheme outperforms cloud-based computing schemes in terms of response time, and meets SLA requirements for real-time IoMT systems. Finally, we discuss challenges to realising real-time IoMT systems in the Internet of Things Era.