We propose an online JSPTO method that integrates the consideration of service startup delay to enhance task offloading efficiency.

We propose an adaptive multi-server collaborative MEC approach tailored for heterogeneous edge environments to achieve efficient AIGC by dynamically allocating task workload across multiple ESs.

We propose a novel AIGC Task Scheduling (DDRL-ATS) algorithm based on Diffusion Deep Reinforcement Learning, achieving efficient AIGC tailored for heterogeneous MEC environments.

We propose an online Deep Reinforcement Learning-based Simultaneous Multi-ES Offloading (DRL-SMO) algorithm along with a top-k deep Q-learning network model, thus simultaneously enabling multiple ESs’ idle resources to accelerate task processing.

We propose a novel approach called SMCoEdge, which utilizes simultaneous multi-ES offloading to minimize the make-span of task offloading for computation-intensive IoT applications.

We propose a novel Reinforcement Learning based Transmission Revoking Approach (RL-TRA) to address this problem aiming at fairer utilization of unlicensed band restrained by URLLC.

We propose a mechanism-image fusion approach to calibration of a US-guided dual-arm robotic brachytherapy system.

In this paper, a restricted traversal method is defined to identify the strong trust paths from the truster and the trustee. Then, these paths are aggregated to predict the trust rate between them. During the traversal on a social network, interest topics, and topology features are comprehensively considered, where weighted interest topics are used to measure the semantic similarity between users.

We propose some dynamic complexity metrics for service systems through measuring their execution behaviors.