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Sensing and Mobile Edge Computing
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Sensing and Mobile Edge Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 765

Special Issue Editor

Dr. Hai Dong

E-Mail Website
Guest Editor
School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia
Interests: services computing; distributed computing; cybersecurity; machine learning; data analysis; social sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In order to keep up with the rapid pace of human life, sensing technology integrated with wireless networks has to overcome many issues regarding communications (e.g., short communication range, security, privacy, reliability, mobility, etc.), and resources (e.g., power considerations, storage capacity, processing capabilities, bandwidth availability, etc.). As a promising edge technology, mobile edge computing (MEC) is seen as the solution for this. It is an important component in the 5G architecture which supports a variety of innovative applications and services where ultra-low latency is required. On the one hand, mobile edge computing provides an IT service environment and cloud computing capabilities at the edge of the mobile network, within the radio access network (RAN) and in close proximity to mobile subscribers. On the other hand, MEC connects the user directly to the cloud service-enabled edge network. Deploying MEC at the base station enhances computation and avoids bottlenecks and system failure. In addition, recently, artificial intelligence (AI) is undergoing a sustained success renaissance as it can substantially improve networks' cognitive performance and intelligence, thereby contributing to fully unleashing the potential of big data. Pushing the AI frontiers to the network edge in this context has given rise to an emerging interdiscipline, namely edge intelligence (EI).

In the 5G era, sensing and AI-empowered MEC applications will extend to transportation systems, intelligent driving, real-time haptic control, augmented reality, and other fields. This Special Issue seeks to bring together research that sheds light on the ways in which sensing technologies, AI technologies, and edge computing will shape the future of the next generation of information technology. Topics of interest include, but are not limited to, the following:

  • Next-generation sensing technologies built around cloud computing capabilities;
  • Mobile edge computing;
  • Edge Intelligence/Deep Edge Intelligence;
  • AI model compression, pruning, and efficiency on edge devices;
  • Edge computing for smart materials;
  • Edge computing for intelligent IoT;
  • Edge computing for embedded systems;
  • Edge-computing-based applications for the IoT and Wireless Sensor Networks;
  • Modeling, system architecture, and deployment for edge-based 6G applications;
  • Security, privacy, and trust of edge-based sensing technologies;
  • Edge-/Fog-based sensing services.

Dr. Hai Dong
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Published Papers (1 paper)

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Research

19 pages, 1807 KiB  
Article
Adaptive Dataset Management Scheme for Lightweight Federated Learning in Mobile Edge Computing
by Jingyeom Kim, Juneseok Bang and Joohyung Lee
Sensors 2024, 24(8), 2579; https://doi.org/10.3390/s24082579 - 18 Apr 2024
Viewed by 343
Abstract
Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the [...] Read more.
Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback–Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy. Full article
(This article belongs to the Special Issue Sensing and Mobile Edge Computing)
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