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Embedded Artificial Intelligence (AI) for Smart Sensing and IoT Applications
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Embedded Artificial Intelligence (AI) for Smart Sensing and IoT Applications

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

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 28625

Special Issue Editors

Prof. Dr. Alexander Wong

E-Mail Website
Guest Editor
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: computer vision; medical imaging; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Parthipan Siva

E-Mail Website
Guest Editor
Chirp Inc
Interests: computer vision; embedded AI; video analytics; smart sensors
Dr. George Shaker

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
Interests: antennas & propagation; RF engineering; UAV wireless communications; mm-waves; sensors; energy harvesting systems; biomedical engineering; vehicle and UAV wireless communications; navigation systems; telematics systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jie Liang

E-Mail Website
Guest Editor
Simon Fraser University
Interests: video processing; embedded computer vision; deep learning

Special Issue Information

Dear Colleagues,

Smart sensing has become increasingly important in modern society given the proliferation and ubiquity of embedded sensors in a huge range of devices ranging from smartphones and smart city infrastructure to consumer IoT devices and health monitoring systems. A key to enabling smart sensing is artificial intelligence, which enables the plethora of sensing data to be processed in a way that gives context and meaningful insights for aiding smart devices to make decisions and perform tasks. There are a number of challenges in developing and integrating artificial intelligence into smart sensing and IoT applications, ranging from memory footprint and computational complexity to privacy and robustness. This Special Issue covers topics related to embedded artificial intelligence for enabling smart sensing and IoT applications, covering such topics as: memory-efficient AI algorithms, computationally efficient AI algorithms, AI privacy, AI security, and embedded AI applications.

Prof. Dr. Alexander Wong
Dr. Parthipan Siva
Prof. Dr. George Shaker
Prof. Dr. Jie Liang
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.

Published Papers (3 papers)

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Research

12 pages, 485 KiB  
Communication
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
by Saad Abbasi, Mahmoud Famouri, Mohammad Javad Shafiee and Alexander Wong
Sensors 2021, 21(14), 4805; https://doi.org/10.3390/s21144805 - 14 Jul 2021
Cited by 9 | Viewed by 2587
Abstract
Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational [...] Read more.
Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30× lower latency than published networks. Full article
(This article belongs to the Special Issue Embedded Artificial Intelligence (AI) for Smart Sensing and IoT Applications)
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32 pages, 755 KiB  
Article
Quantization and Deployment of Deep Neural Networks on Microcontrollers
by Pierre-Emmanuel Novac, Ghouthi Boukli Hacene, Alain Pegatoquet, Benoît Miramond and Vincent Gripon
Sensors 2021, 21(9), 2984; https://doi.org/10.3390/s21092984 - 23 Apr 2021
Cited by 71 | Viewed by 20802
Abstract
Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object [...] Read more.
Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human Activity Recognition. However, there is still room for optimization of deep neural networks onto embedded devices. These optimizations mainly address power consumption, memory and real-time constraints, but also an easier deployment at the edge. Moreover, there is still a need for a better understanding of what can be achieved for different use cases. This work focuses on quantization and deployment of deep neural networks onto low-power 32-bit microcontrollers. The quantization methods, relevant in the context of an embedded execution onto a microcontroller, are first outlined. Then, a new framework for end-to-end deep neural networks training, quantization and deployment is presented. This framework, called MicroAI, is designed as an alternative to existing inference engines (TensorFlow Lite for Microcontrollers and STM32Cube.AI). Our framework can indeed be easily adjusted and/or extended for specific use cases. Execution using single precision 32-bit floating-point as well as fixed-point on 8- and 16 bits integers are supported. The proposed quantization method is evaluated with three different datasets (UCI-HAR, Spoken MNIST and GTSRB). Finally, a comparison study between MicroAI and both existing embedded inference engines is provided in terms of memory and power efficiency. On-device evaluation is done using ARM Cortex-M4F-based microcontrollers (Ambiq Apollo3 and STM32L452RE). Full article
(This article belongs to the Special Issue Embedded Artificial Intelligence (AI) for Smart Sensing and IoT Applications)
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20 pages, 1546 KiB  
Article
An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals
by Inyeop Choi and Hyogon Kim
Sensors 2020, 20(14), 4044; https://doi.org/10.3390/s20144044 - 21 Jul 2020
Cited by 2 | Viewed by 2659
Abstract
The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the [...] Read more.
The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI. Full article
(This article belongs to the Special Issue Embedded Artificial Intelligence (AI) for Smart Sensing and IoT Applications)
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