Publications by authors named "Huaiwen He"
Article Synopsis
- Device-to-device (D2D) technology enhances communication by enabling mobile devices to offload tasks directly between each other, optimizing idle resources.
- The paper introduces a new algorithm that utilizes multi-agent deep reinforcement learning to manage task offloading in mobile edge computing (MEC) systems, aiming to reduce delays and meet deadlines for time-sensitive tasks.
- Extensive simulations show that this innovative approach decreases average task completion delays by 11% and dropped tasks by 17% compared to existing methods, making it especially useful for sensor networks handling large data volumes.
Article Synopsis
- * The study focuses on improving user experience by minimizing shrinkage ratio through optimal decision-making in UAV operation, resource allocation, and task management in complex IoT environments.
- * A two-tier optimization strategy is introduced, using algorithms for UAV trajectory determination and resource allocation, showing significant efficiency improvements in simulation results compared to traditional methods.
Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively.
View Article and Find Full Text PDFUnderstanding human periodic behaviors is crucial in many applications. Existing research has shown the existence of periodicity in human behaviors, but has achieved limited success in leveraging location periodicity and obtaining satisfactory accuracy for oscillations in human periodic behaviors. In this article, we propose the Mobility Intention and Relative Entropy (MIRE) model to address these challenges.
View Article and Find Full Text PDFBacterial cellulose (BC) is a green, natural biopolymer with excellent biocompatibility and a film-forming ability. However, its lack of inherent antibacterial activity restricts its application in medical materials and food preservation. In this study, BC derived from the juice of discarded Xinhui citrus was obtained through fermentation and further modified in situ with graphene oxide (GO) to obtain BC(GO).
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