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AI Applications in Smart Networks and Sensor Devices
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AI Applications in Smart Networks and Sensor Devices

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 15682

Special Issue Editors

Prof. Dr. Jae-Hoon Kim

E-Mail Website
Guest Editor
1. Dept. of AI Convergence Nework, Ajou University, Suwon, Korea
2. Dept. of Industrial Engineering, Ajou University, Suwon 16499, Korea
Interests: AI network applications; edge computing architecture; Internet of Things; blockchain security
Special Issues, Collections and Topics in MDPI journals
Dr. Woon-Young Yeo

E-Mail Website
Guest Editor
Dept. of Information and Communications, Sejong University, Seoul 05006, Korea
Interests: mobile networks; access control; network simulations
Dr. Ricardo Jardim Goncalves

E-Mail Website
Guest Editor
Faculty of Sciences and Technology, New University of Lisbon, UNINOVA, 2829-516 Caparica, Portugal
Interests: Interoperability of Complex Systems and Applications; Internet of Things; Smart Sensors; Industry 4.0; eHealth
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine-generated data expansion is a global phenomenon in recent internet services. The proliferation of mobile communication and smart sensor devices has increased the utilization of machine-generated data significantly. According to Statista, there are about 30.9 billion interconnected devices in the world (until 2025). Their number is set to explode in the following years as internet consumption rises and new gadgets and machinery hit the market. With recent innovative network and chip technology, smart devices are becoming smarter with high computing power, bandwidth, and storage available on the devices. The data expansion and smart devices make the high availability of artificial intelligence on the massive networks. Edge networking for smart devices accelerates the usability of intelligent applications. This Special Issue focuses on the analysis, design, and implementation of AI-powered solutions over the smart networks and sensor devices.

The topics of interest include but are not limited to:

  • Smart city applications;
  • Smart factory applications;
  • AI computing infrastructure;
  • On-device machine learning;
  • Decentralized applications;
  • AI for mobile network;
  • Real-time computer processing on cognition;
  • AI for Edge computing;
  • Low-power AI for sensor systems;
  • Distributed inferencing and learning;
  • AI powered security ;
  • Blockchain for IoT and mobile devices.

Prof. Dr. Jae-Hoon Kim
Dr. Woon-Young Yeo
Prof. Dr. Ricardo Jardim Goncalves
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 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.

Keywords

  • artificial intelligence
  • smart city
  • smart network
  • decentralized application
  • sensor devices

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Published Papers (4 papers)

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Research

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17 pages, 3418 KiB  
Article
A Fast and Low-Power Detection System for the Missing Pin Chip Based on YOLOv4-Tiny Algorithm
by Shiyi Chen, Wugang Lai, Junjie Ye and Yingjie Ma
Sensors 2023, 23(8), 3918; https://doi.org/10.3390/s23083918 - 12 Apr 2023
Cited by 1 | Viewed by 2114
Abstract
In the current chip quality detection industry, detecting missing pins in chips is a critical task, but current methods often rely on inefficient manual screening or machine vision algorithms deployed in power-hungry computers that can only identify one chip at a time. To [...] Read more.
In the current chip quality detection industry, detecting missing pins in chips is a critical task, but current methods often rely on inefficient manual screening or machine vision algorithms deployed in power-hungry computers that can only identify one chip at a time. To address this issue, we propose a fast and low-power multi-object detection system based on the YOLOv4-tiny algorithm and a small-size AXU2CGB platform that utilizes a low-power FPGA for hardware acceleration. By adopting loop tiling to cache feature map blocks, designing an FPGA accelerator structure with two-layer ping-pong optimization as well as multiplex parallel convolution kernels, enhancing the dataset, and optimizing network parameters, we achieve a 0.468 s per-image detection speed, 3.52 W power consumption, 89.33% mean average precision (mAP), and 100% missing pin recognition rate regardless of the number of missing pins. Our system reduces detection time by 73.27% and power consumption by 23.08% compared to a CPU, while delivering a more balanced boost in performance compared to other solutions. Full article
(This article belongs to the Special Issue AI Applications in Smart Networks and Sensor Devices)
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12 pages, 5793 KiB  
Article
Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset
by Zhehui Yang, Chenbo Zhao, Hiroya Maeda and Yoshihide Sekimoto
Sensors 2022, 22(24), 9992; https://doi.org/10.3390/s22249992 - 19 Dec 2022
Cited by 5 | Viewed by 3390
Abstract
The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become [...] Read more.
The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become an essential tool for HD map reconstruction and advanced driver-assistance systems (ADASs). Therefore, the performance evaluation and comparison of the latest deep-learning algorithms in this field is indispensable. However, most existing works in this area limit their focus to the detection of individual targets, such as vehicles or pedestrians and traffic signs, from driving view images. In this study, we present a systematic comparison of three recent algorithms for large-scale multi-class road facility detection, namely Mask R-CNN, YOLOx, and YOLOv7, on the Mapillary dataset. The experimental results are evaluated according to the recall, precision, mean F1-score and computational consumption. YOLOv7 outperforms the other two networks in road facility detection, with a precision and recall of 87.57% and 72.60%, respectively. Furthermore, we test the model performance on our custom dataset obtained from the Japanese road environment. The results demonstrate that models trained on the Mapillary dataset exhibit sufficient generalization ability. The comparison presented in this study aids in understanding the strengths and limitations of the latest networks in multiclass object detection on large-scale street-level datasets. Full article
(This article belongs to the Special Issue AI Applications in Smart Networks and Sensor Devices)
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21 pages, 1089 KiB  
Article
Mitigating Cold Start Problem in Serverless Computing with Function Fusion
by Seungjun Lee, Daegun Yoon, Sangho Yeo and Sangyoon Oh
Sensors 2021, 21(24), 8416; https://doi.org/10.3390/s21248416 - 16 Dec 2021
Cited by 24 | Viewed by 4911
Abstract
As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off [...] Read more.
As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows. Full article
(This article belongs to the Special Issue AI Applications in Smart Networks and Sensor Devices)
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Review

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27 pages, 5496 KiB  
Review
Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network
by Hyunho Seok, Shihoon Son, Sagar Bhaurao Jathar, Jaewon Lee and Taesung Kim
Sensors 2023, 23(6), 3118; https://doi.org/10.3390/s23063118 - 14 Mar 2023
Cited by 12 | Viewed by 4371
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental [...] Read more.
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks. Full article
(This article belongs to the Special Issue AI Applications in Smart Networks and Sensor Devices)
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