Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11281042 | PMC |
| http://dx.doi.org/10.3390/s24144677 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multi-UAV-Assisted Task Offloading and Trajectory Optimization for Edge Computing via NOMA.
Sensors (Basel)
August 2025
Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, China.
Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA).
View Article and Find Full Text PDFThe Role of Community Health Workers in Improving Diabetes Device Use Among Youth With Type 1 Diabetes: A Web-Based Qualitative Study Using Human-Centered Design With Clinicians.
JMIR Diabetes
August 2025
Albert Einstein College of Medicine, Bronx, NY, United States.
Background: Inequity in diabetes technology use persists among Black and Hispanic youth with type 1 diabetes (T1D). Community health workers (CHWs) can address social and clinical barriers to diabetes device use. However, more information is needed on clinicians' perceptions to inform the development of a CHW model for youth with T1D.
View Article and Find Full Text PDFResearch on Computation Offloading and Resource Allocation Strategy Based on MADDPG for Integrated Space-Air-Marine Network.
Entropy (Basel)
July 2025
College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China.
This paper investigates the problem of computation offloading and resource allocation in an integrated space-air-sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges.
View Article and Find Full Text PDFMulti-dimensional flux balance analysis to optimized resources and energy efficiency in MEC aided 5G networks.
Sci Rep
August 2025
Wireless Sensor Networks Lab, Department of Electronics & Communication Engineering, National Institute of Technology, Patna, Bihar, 500008, India.
5G and 6G development aim to fulfil very low latency, low energy consumption, and great computation ability. In the present era, the number of devices is increasing daily, which requires more communication and computation. Device-to-device (D2D), relay server, and mobile edge computing (MEC) systems were developed to meet these objectives.
View Article and Find Full Text PDFMulti video stream collaborative adaptive offloading scheme based on equilibrium game theory.
Sci Rep
August 2025
Department of Electronic Information Engineering, International Union College, Dalian Maritime University, Dalian, Liaoning, China.
Massive video stream transmission and analysis require a large amount of bandwidth and computing resources, which poses a serious challenge to the current video stream offloading scheme based on mobile edge computing (MEC). A self-adaptive offloading scheme based on a balanced game multi video stream collaborative optimization framework is proposed for this purpose. Firstly, under the constraint of long-term MEC energy budget, the processing cost of video tasks is minimized by jointly optimizing data stream selection decisions, server offloading decisions, bandwidth resource allocation, and computing resource allocation.
View Article and Find Full Text PDF