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Edge Computing and Deep Learning for Smart IoT Systems

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

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 4846

Special Issue Editors

Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia
Interests: cloud computing; intelligent transport systems; software engineering
School of Information Science and Technology, Beijing Forestry University, Beijing, China
Interests: deep learning; big data analysis; software security

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China
Interests: AI for software engineering; software engineering for AI; internet of things

Special Issue Information

Dear Colleagues,

Smart Internet of Things (IoT) systems are playing a significant role in our society for enabling smart home, smart logistics, smart manufacturing, smart health, smart agriculture, among others. Nowadays, the integration of edge computing with artificial intelligence, particularly deep learning, has become the backbone for smart IoT systems. On one hand, edge computing, as compared with centralised cloud computing, has unique features, such as the physical proximity to the end devices and users, which bring many benefits, such as low latency, energy efficiency, privacy protection, reduced bandwidth consumption, on-premises, and context awareness. On the other hand, deep learning has consistently shown high capabilities in reasoning and analysis, and, thus, widely used in various domains, such as computer vision and image classification. In this Special Issue, we encourage submission of papers that describe original, high-quality, empirically and/or theoretically validated work in the application, implementation and evaluation of edge computing and deep learning for the development, deployment and maintenance of smart IoT systems.

Dr. Huai Liu
Dr. Bo Yang
Dr. Rubing Huang
Guest Editors

Manuscript Submission Information

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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

  • smart systems
  • Internet of Things
  • edge computing
  • deep learning
  • IoT architecture design
  • smart system development
  • edge deployment solution
  • quality of edge services

Published Papers (2 papers)

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Research

21 pages, 1076 KiB  
Article
Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach
by Zaipeng Xie, Cheng Ji, Lifeng Xu, Mingyao Xia and Hongli Cao
Sensors 2023, 23(12), 5447; https://doi.org/10.3390/s23125447 - 8 Jun 2023
Cited by 4 | Viewed by 2093
Abstract
The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge [...] Read more.
The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications. Full article
(This article belongs to the Special Issue Edge Computing and Deep Learning for Smart IoT Systems)
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15 pages, 4648 KiB  
Article
An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT
by Kunpeng Song, Jiajia Fang, Lei Zhang, Fangni Chen, Jian Wan and Neal Xiong
Sensors 2022, 22(17), 6458; https://doi.org/10.3390/s22176458 - 27 Aug 2022
Cited by 6 | Viewed by 2244
Abstract
Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries [...] Read more.
Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients’ quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively. Full article
(This article belongs to the Special Issue Edge Computing and Deep Learning for Smart IoT Systems)
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