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Future Internet
Special Issues
Task Offloading and Resource Scheduling in Mobile Edge-Cloud Computing
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Task Offloading and Resource Scheduling in Mobile Edge-Cloud Computing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 264

Special Issue Editors

Prof. Dr. Gaocai Wang

E-Mail Website
Guest Editor
School of Computer and Electronics Information, Guangxi University, Nanning 530004, China
Interests: computer network technology; wireless network technology; algorithm optimization
Prof. Dr. Shuqiang Huang

E-Mail Website
Guest Editor
School of Science and Engineering, Jinan University, Guangzhou 510632, China
Interests: privacy computing; artificial intelligence security; industrial IoT security

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the cutting-edge cross-disciplinary field of Task Offloading and Resource Scheduling in Mobile Edge-Cloud Computing. Against the backdrop of the booming development of 5G/6G, the Internet of Things, and autonomous driving, edge-cloud collaborative systems are facing unprecedented challenges in low-latency task execution, efficient resource allocation, and dynamic service adaptation.

We invite researchers and engineers worldwide to submit original research papers, innovative technical reports, and insightful review articles covering (but not limited to) optimization algorithms, intelligent decision-making models, performance evaluation mechanisms, and practical applications of task offloading and resource scheduling.

Prof. Dr. Gaocai Wang
Prof. Dr. Shuqiang Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • task offloading
  • resource scheduling
  • low latency
  • energy efficiency
  • mobile edge-cloud computing

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Published Papers (1 paper)

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Research

23 pages, 1168 KB  
Article
A Task Scheduling and Management Platform for Multi-Workload Smart Elderly Care on Pure-Edge CPU-TPU Heterogeneous Nodes
by Tuo Nie, Dajiang Yang, Xin Guo, Wenxuan Zhu and Bochao Su
Future Internet 2026, 18(5), 242; https://doi.org/10.3390/fi18050242 - 1 May 2026
Abstract
Smart care applications impose increasingly stringent requirements on low-latency execution, privacy preservation, and continuous monitoring. These requirements are driving intelligent services from cloud-centric architectures toward edge-side deployment. When multiple care-related workloads are deployed on resource-constrained edge devices, performance bottlenecks arise not only from [...] Read more.
Smart care applications impose increasingly stringent requirements on low-latency execution, privacy preservation, and continuous monitoring. These requirements are driving intelligent services from cloud-centric architectures toward edge-side deployment. When multiple care-related workloads are deployed on resource-constrained edge devices, performance bottlenecks arise not only from model inference itself, but also from process scheduling, inter-process communication, and resource coordination overhead. To address this issue, this paper presents a task scheduling and management platform for multi-workload smart elderly care on a single pure-edge CPU–TPU heterogeneous node. The platform adopts a shared-memory and event-driven synchronization mechanism together with fine-grained process partitioning, thereby establishing a data-sharing and runtime-coordination framework for concurrent multi-workload execution. To evaluate the effectiveness of the proposed platform, experiments were conducted under single-workload, multi-workload, multi-resolution, and long-term runtime settings. The results show that, compared with two baseline schemes, the proposed platform improves the average frame rate by 66.7% and 71.1%, reduces net memory usage by 96.3% and 45.3%, and lowers net power consumption by 46.8% and 37.7%, respectively, under the single-workload setting. Under 10 concurrent workload instances, the system still maintains a stable frame rate of 42.03 ± 0.73 fps, demonstrating strong concurrency scalability. Multi-resolution experiments further indicate that the performance degradation at higher resolutions is mainly constrained by the front-end data supply stage. A continuous 10-day runtime experiment additionally verifies the sustained operating capability and resource stability of the platform under pure-edge deployment. These results demonstrate that node-level shared-memory and event-driven coordination can effectively improve the execution efficiency, scalability, and stability of real-time multi-workload analytics on such pure-edge heterogeneous nodes, providing a useful basis for future extensions to multi-node edge environments and edge–cloud collaborative task scheduling. Full article
(This article belongs to the Special Issue Task Offloading and Resource Scheduling in Mobile Edge-Cloud Computing)
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