With the decreasing cost of satellite launch, Low Earth Orbit (LEO) satellite constellations have become an important part to complement the terrestrial communication systems for seamless coverage, limited latency, and high throughput. Meanwhile, the developing hardware computation capacity and Inter-Satellite Links (ISLs) have enabled LEO satellites to collaboratively conduct the onboard data processing, which is significantly important for the on-orbit Earth observation, environmental monitoring, and space exploration. However, the highly dynamic topology and heterogeneous resource distribution of LEO networks lead to traditional terrestrial scheduling mechanisms being ineffective. To address these challenges, this paper proposes a collaborative computation offloading framework based on the Software Defined Networking (SDN) technique for heterogeneous LEO satellite networks. The logically centralized controllers obtain global network states (e.g., satellite computing load and ISL conditions) to enable flexible-adaptive task scheduling. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed to decide the task splitting ratio, task processing satellites, and result aggregation satellite under dynamic network conditions. The simulation results demonstrate that the proposed MADDPG strategy significantly reduces the average task completion latency and improves the task success rate compared to benchmarks.

Parkinson's Disease (PD) treatment is challenging due to symptom heterogeneity and the lack of a definitive cure. Lifelong medication requires personalized treatment plans developed by physicians, but such approaches are constrained by high costs and limited physician capacity. Although deep learning (DL) methods have been explored, they lack interpretability and are restricted to numerical data inputs. In this study, we propose a novel framework that leverages large language models (LLMs) to design personalized PD treatment strategies, integrating both patient information in natural language form and external textual knowledge sources (e.g., medical guidelines). To enhance effectiveness, we use Monte Carlo Tree Search (MCTS) to refine strategies and establish a robust medication recommendation dataset. To enhance reliability and interpretability, we incorporate Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning within the LLM system, ensuring that each proposed strategy is accompanied by step-by-step explanations and references to similar historical cases. Experimental evaluations using the Parkinson's Progression Marking Initiative (PPMI) dataset show that our method surpasses physician-prescribed treatments, achieving an average reduction of over 1.4 points in the revised unified Parkinson's disease rating scale part III (MDS-UPDRS-III) scores. Our method also outperforms the RL-method by 1.01 points on average. Furthermore, over 43% of patients achieve more than 2 point-reduction of MDS-UPDRS-III scores. A detailed case study highlights the flexibility of LLMs in dynamically adjusting medication plans for patients at different disease stages, highlighting its potential to advance personalized PD management in real-world settings.

With the rapid growth of Internet of Medical Things (IoMT) devices and the advancement of medical large language models, smart healthcare applications, including regular vital sign monitoring, medical intervention, and medication adherence tracking, play an increasingly important role in the near future. As the data generated by smart healthcare applications usually have strict requirements for latency and privacy, traditional data processing in a centralized manner is not the preferred choice, especially for users in remote areas. In this article, we consider the nonterrestrial networks composed of Low Earth Orbit satellites and autonomous aerial vehicles (AAVs) to realize seamless coverage. A federated reinforcement learning-based hierarchical computation offloading strategy is proposed to make distinct decisions and protect data privacy. Moreover, we optimize the communication efficiency between IoMT devices, AAVs, and satellites, improve task processing capabilities, and reduce network load by reasonably setting task priorities. The simulation results show that the proposed method performs well in reducing energy consumption and shortening task completion time, significantly improving resource utilization and achieving system load balancing.

In Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users’ tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques.

UAV-assisted MEC revolutionizes edge computing by deploying UAVs for real-time data processing in areas lacking infrastructure, supporting a wide range of applications from emergency responses to smart cities. Unlike edge servers, UAVs face substantial computational constraints, necessitating a comprehensive strategy that integrates UAV trajectory with task offloading, caching, and migration. Existing studies often overlook the synergy among these strategies, impacting their overall effectiveness. Furthermore, the focus on content pre-caching overlooks task caching’s critical role in addressing high computational demands with limited UAV resources. This research aims to jointly optimize UAV trajectories and task management strategies, including offloading, caching, and migration. Utilizing the Lyapunov optimization framework, we break down the complex optimization problem into manageable subproblems: UAV placement, user-UAV association, task offloading, scheduling, and bandwidth allocation, addressed iteratively using the Block Coordinate Descent method. Specifically, the scheduling subproblem is transformed into a non-convex quadratically constrained quadratic programming problem, managed effectively through semidefinite relaxation and a probabilistic mapping approach. Our simulations show that this integrated approach significantly boosts system throughput and reduces execution times compared to conventional methods. This study enhances the understanding of the interplay between UAV trajectory planning and task management, offering vital theoretical insights for advancing UAV-assisted MEC systems.

Recent research in price-based Mobile Edge Computing (MEC) has predominantly aimed at maximizing edge server revenue by efficiently allocating computing resources. While this operational approach has certainly strengthened the edge computing industry, the practical deployment scenario's impact on server hardware lifespan has not been fully explored. In this paper, we introduce a novel server pricing strategy that accounts for the influence of CPU temperature on the server's longevity, all while ensuring the quality of service requirements for time-sensitive User Equipments (UEs). Leveraging these insights, we establish an MEC system model with a single MEC server and multiple UEs, which we then formulate as a Stackelberg game. We derive two closed-form solutions considering various UEs' conditions, closely aligning with the expectations of time-sensitive UEs. Our simulation results showcase the effectiveness of our proposed method in safeguarding hardware equipment while minimizing revenue loss.

Due to high flexibility and ease of deployment, Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide services for users to meet the demands of computing-intensive tasks at edge. However, the MEC-server mounted UAV is inappropriate for heavy-computation tasks owing to the limitation of energy supply and hardware cost, which may make for excessively high CPU temperature. To tackle this issue, this paper considers a UAV-and-basestation (BS) hybrid-assisted MEC network, where a hover-fly-hover mode is adopted to facilitate the provisioning of MEC services with the help of BS. Furthermore, a temperature control constraint is introduced to ensure the reliability of CPU at UAV. We aim to minimize the cruise duration of UAV with thermal-aware constraint by jointly optimizing UAV hovering trajectory, task scheduling strategy, and computation-and-communication resource allocation strategy. Although the formulated problem is non-convex, we decouple it into three subproblems and solve them in an iterative manner. Simulation results demonstrate that the proposed algorithm can not only satisfy the CPU temperature constraint but also help save the cruise duration.