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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: closed (30 September 2024) | Viewed by 8202

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.

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Published Papers (5 papers)

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Research

18 pages, 1860 KiB  
Article
Progressive Bounded Error Piecewise Linear Approximation with Resolution Reduction for Time Series Data Compression
by Jeng-Wei Lin, Shih-wei Liao, Yu-Hung Tsai and Ching-Che Huang
Sensors 2025, 25(1), 145; https://doi.org/10.3390/s25010145 - 29 Dec 2024
Viewed by 950
Abstract
Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to [...] Read more.
Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy. When a certain level of data accuracy is sacrificed, lossy compression technologies can achieve better compression ratios. However, different applications may have different requirements for data accuracy. Instead of keeping multiple compressed versions of a time series w.r.t. different error bounds, HIRE hierarchically maintains a tree, where the root records a constant function to approximate the whole time series, and each other node records a constant function to approximate a part of the residual function of its parent for a particular time period. To retrieve data w.r.t. a specific error bound, it traverses the tree from the root down to certain levels according to the requested error bound and aggregates the constant functions on the visited nodes to generate a new bounded error compressed version dynamically. However, the number of nodes to be visited is unknown before the tree traversal completes, and thus the data size of the new version. In this paper, a time series is progressively decomposed into multiple piecewise linear functions. The first function is an approximation of the original time series w.r.t. the largest error bound. The second function is an approximation of the residual function between the original time series and the first function w.r.t. the second largest error bound, and so forth. The sum of the first, second, …, and m-th functions is an approximation of the original time series w.r.t. the m-th error bound. For each iteration, Swing-RR is used to generate a Bounded Error Piecewise Linear Approximation (BEPLA). Resolution Reduction (RR) plays an important role. Eight real-world datasets are used to evaluate the proposed method. For each dataset, approximations w.r.t. three typical error bounds, 5%, 1%, and 0.5%, are requested. Three BEPLAs are generated accordingly, which can be summed up to form three approximations w.r.t. the three error bounds. For all datasets, the total data size of the three BEPLAs is almost the same with the size used to store just one version w.r.t. the smallest error bound and significantly smaller than the size used to keep three independent versions. The experiment result shows that the proposed method, referred to as PBEPLA-RR, can achieve very good compression ratios and provide multiple approximations w.r.t. different error bounds. Full article
(This article belongs to the Special Issue Sensing and Mobile Edge Computing)
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28 pages, 5225 KiB  
Article
MAARS: Multiagent Actor–Critic Approach for Resource Allocation and Network Slicing in Multiaccess Edge Computing
by Ducsun Lim and Inwhee Joe
Sensors 2024, 24(23), 7760; https://doi.org/10.3390/s24237760 - 4 Dec 2024
Viewed by 1055
Abstract
This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. Network slicing divides a physical network into virtual slices, each tailored to efficiently allocate resources and meet diverse service requirements. To maximize the completion [...] Read more.
This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. Network slicing divides a physical network into virtual slices, each tailored to efficiently allocate resources and meet diverse service requirements. To maximize the completion rate of user-computing tasks within these slices, the problem is decomposed into two subproblems: efficient core-to-edge slicing (ECS) and autonomous resource slicing (ARS). ECS facilitates collaborative resource distribution through cooperation among edge servers, while ARS dynamically manages resources based on real-time network conditions. The proposed solution, a multiagent actor–critic resource scheduling (MAARS) algorithm, employs a reinforcement learning framework. Specifically, MAARS utilizes a multiagent deep deterministic policy gradient (MADDPG) for efficient resource distribution in ECS and a soft actor–critic (SAC) technique for robust real-time resource management in ARS. Simulation results demonstrate that MAARS outperforms benchmark algorithms, including heuristic-based, DQN-based, and A2C-based methods, in terms of task completion rates, resource utilization, and convergence speed. Thus, this study offers a scalable and efficient framework for resource optimization and network slicing in MEC networks, providing practical benefits for real-world deployments and setting a new performance benchmark in dynamic environments. Full article
(This article belongs to the Special Issue Sensing and Mobile Edge Computing)
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26 pages, 9112 KiB  
Article
On Construction of Real-Time Monitoring System for Sport Cruiser Motorcycles Using NB-IoT and Multi-Sensors
by Endah Kristiani, Tzu-Hao Yu and Chao-Tung Yang
Sensors 2024, 24(23), 7484; https://doi.org/10.3390/s24237484 - 23 Nov 2024
Viewed by 1479
Abstract
This study leverages IoT technology to develop a real-time monitoring system for large motorcycles. We collaborated with professional mechanics to define the required data types and system architecture, ensuring practicality and efficiency. The system integrates the NB-IoT for efficient remote data transmission and [...] Read more.
This study leverages IoT technology to develop a real-time monitoring system for large motorcycles. We collaborated with professional mechanics to define the required data types and system architecture, ensuring practicality and efficiency. The system integrates the NB-IoT for efficient remote data transmission and uses MQTT for optimized messaging. It also includes advanced database management and intuitive data visualization for enhancing the user experience. For hardware installation, the system follows strict guidelines to avoid damaging the motorcycle’s original structure, comply with Taiwan’s legal standards, and prevent unauthorized modifications. The implementation of this real-time monitoring system is anticipated to significantly reduce safety risks associated with mechanical failures as it continuously monitors inappropriate driving behaviors and detects mechanical abnormalities in real time. The study indicates that the integration of advanced technologies, such as the NB-IoT and multi-sensor systems, can lead to improved driving safety and operational efficiency. Furthermore, the research suggests that the system’s ability to provide instant notifications and alerts through the platforms’ instant messaging can enhance user responsiveness to potential hazards, thereby contributing to a safer riding experience. Full article
(This article belongs to the Special Issue Sensing and Mobile Edge Computing)
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20 pages, 16803 KiB  
Article
Construction Jobsite Image Classification Using an Edge Computing Framework
by Gongfan Chen, Abdullah Alsharef and Edward Jaselskis
Sensors 2024, 24(20), 6603; https://doi.org/10.3390/s24206603 - 13 Oct 2024
Cited by 2 | Viewed by 2439
Abstract
Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that [...] Read more.
Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge “intelligence” to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet. Full article
(This article belongs to the Special Issue Sensing and Mobile Edge Computing)
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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
Cited by 1 | Viewed by 1211
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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