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Artificial Neural Networks for IoT-Enabled Smart Applications II

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2654

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Special Issue Editors

Dr. Andrei Velichko

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Institute of Physics and Technology, Petrozavodsk State University, 31 Lenina Str., 185910 Petrozavodsk, Russia
Interests: neural networks; entropy, constrained devices; IoT; reservoir computing; ambient intelligence; synchronization of coupled oscillators; switching effect; smart sensors
Special Issues, Collections and Topics in MDPI journals

Dr. Dmitry Korzun

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Department of Computer Science, Institute of Mathematics and Information Technology, Artificial Intelligence Center, Petrozavodsk State University, 31 Lenina Str., 185910 Petrozavodsk, Russia
Interests: ambient intelligence; smart spaces; internet of things; networking; mathematical modeling; performance evaluation; data mining; information services; industrial internet; socio-cyber-physical systems; software engineering
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Prof. Dr. Alexander Meigal

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Department of Human and Animal Physiology, Laboratory of Novel methods in Physiology, Institute of Higher Biomedical Technologies, Medical Institute, Petrozavodsk State University, 31 Lenina Str., 185910 Petrozavodsk, Russia
Interests: nonlinear dynamics of biosignals; motion analysis; extremal environments; microgravity; ageing; parkinsonism; ambient intelligence; smart spaces; Internet of Things; socio-cyberphysical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the age of neural networks and the Internet of Things (IoT), new neural network architectures capable of operating on devices with limited computing power and small memory sizes are becoming an urgent need. Various artificial intelligence (AI) applications in the IoT field include healthcare services, everyday human life, industry and manufacturing, robotics, agriculture, environment monitoring, building construction and maintenance, disaster rescue, machine learning (ML) sensors and many others. This Special Issue is a continuation of our previous Special Issue on the implementation of artificial neural networks (ANN) in the development of IoT-enabled smart applications.

Traditionally, such applications operate in real time using surrounding IoT devices and remote computers. For example, a smart camera operates with detection intervals of 500 ms to recognize objects in a video stream and respond to upcoming events. Data processing in human health requires the immediate processing of physiological parameters acquired from different sensors (heartrate monitoring, glucose monitoring, oxygen saturation, etc.). Commercial smart IoT devices transfer information to the cloud for further data mining. Nevertheless, efficient network connection is not always available, leading to limitations on meeting real-time requirements. The solution to this problem could be the execution of data processing using ANN installed directly on IoT devices (at least partially), following the edge computing paradigm. In this case, the quality of the network connection has less impact. Enabling AI directly is difficult due to the limited computing power and small memory size of IoT devices. Frequently, smart applications need to run on a lightweight OS with a minimal set of libraries, which imposes limitations on the operation of resource-intensive neural networks.

AI technologies for IoT devices and edge computing are required specifically in mobile healthcare (m-Health), as well as in close application domains with humans in the center “smart human sensorics” and ML sensors. Ambient intelligence (AmI) environments are constructed in IoT environments to provide smart services for people based on a real-time analysis of human cognitive, motion, and autonomous functions. Examples include, but are not limited to, the following:

At-home labs for medical observations and analysis during everyday life, not in the specific and limited conditions of a professional hospital medical lab;
Applications for industrial internet when the real-time monitoring of human movement and health parameters support detection of dangerous situations and incorrect activity in technological operation;
Tactile internet and its demand in bionic applications, whereby a person can “touch and perceive” distant physical objects or even virtual (digital) objects through the Internet.

This Special Issue is dedicated to recent developments in the constantly growing field of computing technologies and artificial intelligence algorithms, including new approaches to AI design on edge devices and the organization of modular, feed-forward, distributed, reservoir, recurrent, convolutional, and deep neural networks for various IoT-enabled smart applications.

Dr. Andrei Velichko
Dr. Dmitry Korzun
Prof. Dr. Alexander Meigal
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

IoT environment
internet of medical devices
healthcare-monitoring devices
IoT-based healthcare services
smart IoT-based agriculture
smart IoT-based environment monitoring
smart IoT-based exploration
smart IoT-based disaster rescue
edge computing
mobile computing
constrained devices
deep neural network
distributed neural networks
feed-forward neural network
convolutional neural network
recurrent neural network
reservoir neural network
modular neural network
machine learning sensors

Related Special Issue

Artificial Neural Networks for IoT-Enabled Smart Applications in Sensors (12 articles)

Published Papers (2 papers)

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Research

20 pages, 3879 KiB

Open AccessArticle

Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease

by Maksim Belyaev, Murugappan Murugappan, Andrei Velichko and Dmitry Korzun

Sensors 2023, 23(20), 8609; https://doi.org/10.3390/s23208609 - 20 Oct 2023

Cited by 2 | Viewed by 1487

Abstract

This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson’s disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (A_RKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0–4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that A_RKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an A_RKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD. Full article

(This article belongs to the Special Issue Artificial Neural Networks for IoT-Enabled Smart Applications II)

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13 pages, 4622 KiB

Open AccessCommunication

BSN-ESC: A Big–Small Network-Based Environmental Sound Classification Method for AIoT Applications

by Lujie Peng, Junyu Yang, Longke Yan, Zhiyi Chen, Jianbiao Xiao, Liang Zhou and Jun Zhou

Sensors 2023, 23(15), 6767; https://doi.org/10.3390/s23156767 - 28 Jul 2023

Viewed by 872

Abstract

In recent years, environmental sound classification (ESC) has prevailed in many artificial intelligence Internet of Things (AIoT) applications, as environmental sound contains a wealth of information that can be used to detect particular events. However, existing ESC methods have high computational complexity and are not suitable for deployment on AIoT devices with constrained computing resources. Therefore, it is of great importance to propose a model with both high classification accuracy and low computational complexity. In this work, a new ESC method named BSN-ESC is proposed, including a big–small network-based ESC model that can assess the classification difficulty level and adaptively activate a big or small network for classification as well as a pre-classification processing technique with logmel spectrogram refining, which prevents distortion in the frequency-domain characteristics of the sound clip at the joint part of two adjacent sound clips. With the proposed methods, the computational complexity is significantly reduced, while the classification accuracy is still high. The proposed BSN-ESC model is implemented on both CPU and FPGA to evaluate its performance on both PC and embedded systems with the dataset ESC-50, which is the most commonly used dataset. The proposed BSN-ESC model achieves the lowest computational complexity with the number of floating-point operations (FLOPs) of only 0.123G, which represents a reduction of up to 2309 times in computational complexity compared with state-of-the-art methods while delivering a high classification accuracy of 89.25%. This work can achieve the realization of ESC being applied to AIoT devices with constrained computational resources. Full article

(This article belongs to the Special Issue Artificial Neural Networks for IoT-Enabled Smart Applications II)

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