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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7744

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Special Issue Editors

Dr. Junwei Yan

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Guest Editor

Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan 430070, China
Interests: industrial Internet of Things; digital twin; fiber grating sensor; intelligent monitoring; thermal error compensation; edge intelligence

Dr. Alberto Gotta

E-Mail Website
Guest Editor

Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 56124 Pisa, Italy
Interests: wireless networks; satellite communication; wireless communications; Internet of Things; artificial intelligence; neural networks
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Special Issue Information

Dear Colleagues,

The Internet of Things has been widely used in many fields such as industry, transportation, health, and security. As an emerging information technology, edge computing can effectively improve the intelligence level of the Internet of Things, promoting the digitization, networking and intelligence of sensors and network devices. The integration of edge computing and the Internet of Things will expand the intelligent application of information technology in a wide variety of fields.

Dr. Junwei Yan
Dr. Alberto Gotta
Guest Editors

Manuscript Submission Information

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Keywords

edge computing
Internet of Things
edge intelligence
multi-sensor integration
intelligent information processing
situation awareness

Published Papers (7 papers)

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Research

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17 pages, 2124 KiB

Open AccessArticle

Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities

by Ahmed M. Alwakeel and Abdulrahman K. Alnaim

Sensors 2024, 24(13), 4254; https://doi.org/10.3390/s24134254 - 30 Jun 2024

Viewed by 280

Abstract

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities’ IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework’s high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework’s impressive performance, scalability, and security capabilities. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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17 pages, 4931 KiB

Open AccessArticle

A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT

by Zijun Zhou, Qingsong Ai, Ping Lou, Jianmin Hu and Junwei Yan

Sensors 2024, 24(12), 3967; https://doi.org/10.3390/s24123967 - 19 Jun 2024

Viewed by 328

Abstract

Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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19 pages, 1008 KiB

Open AccessArticle

On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads

by Sara Hong, Yeeun Kim, Jaehyun Nam and Seongmin Kim

Sensors 2024, 24(12), 3774; https://doi.org/10.3390/s24123774 - 10 Jun 2024

Viewed by 373

Abstract

A recent development in cloud computing has introduced serverless technology, enabling the convenient and flexible management of cloud-native applications. Typically, the Function-as-a-Service (FaaS) solutions rely on serverless backend solutions, such as Kubernetes (K8s) and Knative, to leverage the advantages of resource management for underlying containerized contexts, including auto-scaling and pod scheduling. To take the advantages, recent cloud service providers also deploy self-hosted serverless services by facilitating their on-premise hosted FaaS platforms rather than relying on commercial public cloud offerings. However, the lack of standardized guidelines on K8s abstraction to fairly schedule and allocate resources on auto-scaling configuration options for such on-premise hosting environment in serverless computing poses challenges in meeting the service level objectives (SLOs) of diverse workloads. This study fills this gap by exploring the relationship between auto-scaling behavior and the performance of FaaS workloads depending on scaling-related configurations in K8s. Based on comprehensive measurement studies, we derived the logic as to which workload should be applied and with what type of scaling configurations, such as base metric, threshold to maximize the difference in latency SLO, and number of responses. Additionally, we propose a methodology to assess the scaling efficiency of the related K8s configurations regarding the quality of service (QoS) of FaaS workloads. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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27 pages, 2337 KiB

Open AccessArticle

An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves

by Nieves G. Hernandez-Gonzalez, Juan Montiel-Caminos, Javier Sosa and Juan A. Montiel-Nelson

Sensors 2024, 24(5), 1358; https://doi.org/10.3390/s24051358 - 20 Feb 2024

Viewed by 700

Abstract

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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27 pages, 22777 KiB

Open AccessArticle

The Lightweight Anchor Dynamic Assignment Algorithm for Object Detection

by Ping Han, Xujun Zhuang, Huahong Zuo, Ping Lou and Xiao Chen

Sensors 2023, 23(14), 6306; https://doi.org/10.3390/s23146306 - 11 Jul 2023

Cited by 1 | Viewed by 1214

Abstract

Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current object detection models do not consider the aspect ratio of the ground-truth boxes in anchor assignment and are not well-adapted to objects with very different shapes. Therefore, this paper proposes the Lightweight Anchor Dynamic Assignment algorithm (LADA) for object detection. LADA does not change the structure of the original detection model; first, it selects an equal proportional center region based on the aspect ratio of the ground-truth box, then calculates the combined loss of anchors, and finally divides the positive and negative samples more efficiently by dynamic loss threshold without additional models. The algorithm solves the problems of poor adaptability and difficulty in the selection of the best positive samples based on IoU assignment, and the sample assignment for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample assignment algorithms, the LADA algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm, respectively, with the same model structure, which demonstrates the effectiveness of the LADA algorithm. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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27 pages, 1717 KiB

Open AccessArticle

Energy Efficient Node Selection in Edge-Fog-Cloud Layered IoT Architecture

by Rolden Fereira, Chathurika Ranaweera, Kevin Lee and Jean-Guy Schneider

Sensors 2023, 23(13), 6039; https://doi.org/10.3390/s23136039 - 29 Jun 2023

Cited by 6 | Viewed by 2006

Abstract

Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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Review

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29 pages, 2381 KiB

Open AccessReview

Multi-Access Edge Computing (MEC) Based on MIMO: A Survey

by Mengyu Zhu, Shaoshuai Gao, Guofang Tu and Deyuan Chen

Sensors 2023, 23(8), 3883; https://doi.org/10.3390/s23083883 - 11 Apr 2023

Cited by 3 | Viewed by 2123

Abstract

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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