The integration of satellite communications with mobile edge computing (MEC) into space-air-ground integrated networks, known as satellite edge computing (SEC), has become a crucial research field for future communication systems to provide extensive global coverage services. This paper investigates the joint dependent task offloading and resource allocation problem for remote Internet-of-Things (IoT) applications within the SEC architecture. The proposed system leverages unmanned aerial vehicles (UAVs) as mobile access points and edge servers and utilizes low- earth orbit (LEO) satellites and ground stations as cloud computing resources. Multiple applications with dependent tasks from IoT devices (IoTDs) are modeled as directed acyclic graphs (DAGs). To address the challenges of reducing the system cost in UAV-assisted SEC, we first propose a one-to-many matching algorithm to associate IoTDs with UAVs. Then, a multi-application task sequence algorithm is devoted to merging the multiple DAGs and sorting the task order. Finally, a graph-aware asynchronous multi-agent reinforcement learning approach empowers the agents to autonomously discover optimal offloading and resource allocation strategies. Extensive simulations based on real-world datasets demonstrate the effectiveness of the proposed approach in minimizing the system costs while meeting application latency requirements, outperforming other benchmark algorithms.

Being an up-and-coming application scenario of mobile edge computing (MEC), the post-disaster rescue suffers multitudinous computing-intensive tasks but unstably guaranteed network connectivity. In rescue environments, quality of service (QoS), such as task execution delay, energy consumption and battery state of health (SoH), is of significant meaning. This paper studies a multi-user post-disaster MEC environment with unstable 5G communication, where device-to-device (D2D) link communication and dynamic voltage and frequency scaling (DVFS) are adopted to balance each user's requirement for task delay and energy consumption. A battery degradation evaluation approach to prolong battery lifetime is also presented. The distributed optimization problem is formulated into a mixed cooperative-competitive (MCC) multi-agent Markov decision process (MAMDP) and is tackled with recurrent multi-agent Proximal Policy Optimization (rMAPPO). Extensive simulations and comprehensive comparisons with other representative algorithms clearly demonstrate the effectiveness of the proposed rMAPPO-based offloading scheme.