With the rapid development of 6G technology and the evolution of healthcare Internet of Things (IoT), federated learning has gained significant attention for privacy-preserving distributed model training in medical scenarios. However, existing approaches face challenges in addressing device heterogeneity, communication overhead, and data privacy in healthcare IoT environments. This paper proposes a resource-efficient hierarchical collaborative federated learning framework designed for next-generation 6G-enabled healthcare IoT. We introduce an adaptive edge aggregation frequency adjustment mechanism and a contribution-based dynamic model aggregation weight adjustment mechanism to enhance system training efficiency and model performance. Additionally, a resource-balanced client selection algorithm and a self-organizing federated collaborative training approach are adopted to mitigate the impact of device heterogeneity and improve resource utilization. Experimental results on two medical datasets demonstrate that our approach achieves accuracy improvements of 7.96% and 8.69% over standard federated averaging, reaching 91.58% and 87.05% accuracy respectively. The framework reduces communication overhead by up to 48.4% while maintaining effective communication ratios of 78.6% in stable network environments.

The emergence of sixth-generation (6G) wireless networks integrated with Internet of Things (IoT) ecosystems presents unprecedented opportunities for ultra-reliable, low-latency communications while introducing new paradigms for traffic management. As billions of IoT devices generate massive volumes of heterogeneous traffic with diverse quality-of-service requirements, traditional scheduling approaches become inadequate for handling the complexity and scale of 6G-IoT networks. This paper introduces a trustworthy deep learning framework for dynamic priority traffic scheduling in 6G-IoT environments, addressing the critical need for reliable, transparent, and accountable traffic management systems. Our proposed trustworthy dynamic priority traffic scheduling algorithm (T-DPTSA) leverages deep reinforcement learning with built-in trustworthiness mechanisms to optimize traffic flow allocation while ensuring system reliability and user trust. The framework incorporates uncertainty quantification, explainable decision-making processes, and robust performance guarantees to meet the stringent requirements of mission-critical 6G-IoT applications. Through comprehensive evaluation across diverse 6G-IoT scenarios, T-DPTSA demonstrates exceptional integrated performance that fundamentally redefines the relationship between trustworthiness and efficiency in intelligent network management. The algorithm achieves superior convergence stability while simultaneously delivering the lowest system costs, minimal deadline violations, and shortest waiting times among evaluated approaches.

Smart transportation networks face increasing demands for efficiency and sustainability. This study presents a reinforcement learning approach that optimizes eco-driving strategies for connected and automated vehicles (CAVs) in urban environments, with a particular application to healthcare logistics. Specifically, we propose a novel approach using reinforcement learning, specifically a twin delayed deep deterministic policy gradient (TD3) algorithm, to dynamically optimize CAV trajectories at signalized intersections. The proposed healthcare eco-driving trajectory optimization (TD3-HETO) model incorporates real-time traffic conditions, signal timing information, and healthcare urgency levels to generate optimal acceleration profiles. The reward function is designed to balance energy efficiency, traffic flow, safety, comfort, and healthcare delivery timeliness. Additionally, the model introduces a dynamic exploration strategy that adapts to healthcare task urgency, enabling efficient balancing between energy consumption and delivery timelines. Experimental results show that TD3-HETO reduces energy consumption by up to 28.7% compared to baseline methods while improving average speeds by 3.7% for urgent healthcare deliveries. The model achieves superior safety performance with 98.7% of time steps showing zero conflicts, compared to 95.3% for the best baseline. TD3-HETO also demonstrates remarkable adaptability to varying traffic demands and signal timings, maintaining consistent performance even at high traffic volumes. This research contributes to developing intelligent transportation systems to enhance environmental sustainability and healthcare accessibility in smart cities, potentially improving patient outcomes and operational efficiency in urban healthcare logistics.

In healthcare environments, accurate and real-time patient localization and monitoring are crucial for ensuring patient safety and improving operational efficiency. This article proposes DeepSense-Healthcare, a hybrid CNN-LSTM framework integrated with adaptive resource management to enhance near-field (NF) IoT-based patient localization and monitoring in healthcare facilities. By combining convolutional neural networks (CNNs) for spatial feature extraction with long short-term memory (LSTM) networks for temporal modeling, DeepSense-Healthcare captures complex spatial-temporal patterns in NF signals, achieving high localization accuracy. An adaptive resource management module is incorporated to optimize computational load, dynamically adjusting resource allocation based on patient activity levels, thereby improving energy efficiency and maintaining responsiveness. We evaluate the proposed framework against baseline models through extensive experiments on the SEED-VIG and ILM datasets across various activity levels. The results demonstrate that DeepSense-Healthcare outperforms conventional methods in localization accuracy, energy efficiency, and latency during various activity scenarios. These findings underscore the effectiveness of DeepSense-Healthcare as a robust and efficient solution for continuous patient monitoring in dynamic healthcare settings.

The rapid development of the Internet of Things (IoT) and wireless communication technologies has enabled the realization of vehicle-road cooperative systems. However, the vast amount of data generated by IoT devices in these systems poses challenges for traditional data processing methods. Augmented intelligence, such as deep reinforcement learning (DRL), has emerged as a powerful solution for processing large-scale real-time data and making accurate decisions. This article proposes an edge-cloud framework for vehicle-road cooperative traffic signal control in the context of Augmented IoT (AIoT). The framework integrates an edge-cloud collaborative resource allocation algorithm based on DRL and a traffic signal timing method that combines DRL with an extended Kalman filter. Simulation results demonstrate the effectiveness of the proposed framework in improving traffic efficiency and reducing vehicle waiting times. The average queue length was reduced by 35.7%, and the average waiting time increased by 29.1%. The proposed edge-cloud framework for vehicle-road cooperative traffic signal control in AIoT provides a promising solution for enhancing traffic management in smart cities.