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Artificial Intelligence Systems Design for IoT Applications
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Artificial Intelligence Systems Design for IoT Applications

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 11469
Please contact the Guest Editor or the Section Managing Editor at (ava.jiang@mdpi.com) for any queries.

Special Issue Editors

Prof. Dr. Rishad Shafik

E-Mail Website
Guest Editor
Microsystems Research Group, School of Engineering, Newcastle University, Newcastle NE1 7RU, UK
Interests: low-power machine learning; hardware/software co-design; Tsetlin machines
Special Issues, Collections and Topics in MDPI journals
Dr. Maryam Mehrnezhad

E-Mail Website
Guest Editor
School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
Interests: sensor security; applied artificial intelligence
Dr. Ehsan Toreini

E-Mail Website
Guest Editor
Department of Computer ScienceDurham University, Durham DH1 3LE, UK
Interests: physical security; integrity protection mechanisms; trustworthy machine learning

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) systems are at the core of future generations of Internet-of-Things (IoT) applications that constitute our Industrial Revolution 4.0 ambitions. Empowering AI pervasively can transform the way information-led decisions are autonomously made in our day to day life. However, in advancing this technology, significant challenges have surfaced around energy efficiency, security, and dependability.

Modern AI systems have evolved in complexity in terms of the algorithmic advances, hardware capabilities, and interfaces with the real-world through the use of sensors. Algorithmic and hardware capabilities have allowed us to package more powerful computation with high accuracy, while the integration of multi-modal sensing devices has provided opportunities for new applications. Unfortunately, with the complexity of these systems spiraling out of control, the energy costs are also rising significantly, which contradicts the pervasive AI implementation goal.

The consideration of energy efficiency is further intertwined with two major concerns facing users: security and dependability. As many applications closely operate with sensitive information, protection of data is crucial for personal and societal integrity. Likewise, dependability gives the user confidence that the system is designed with the capability of mitigating aberrant situations, such as the presence of faults. However, designing for security and/or dependability is often associated with additional energy costs arising from hardware or software solutions for protection, which further exacerbates system design challenges.

In this Special Issue, we invite papers delineating innovative hardware, software, and systems approaches to addressing the above challenges. We also encourage researchers to provide novel insights covering areas from low-level circuit to cross-layer systems design.

Dr. Rishad Shafik
Dr. Maryam Mehrnezhad
Dr. Ehsan Toreini
Guest Editors

Manuscript Submission Information

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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

  • AI hardware
  • sensor design for AI
  • energy-efficient hardware design
  • new applications and integration methods
  • dependable AI systems design
  • security of sensing and AI systems
  • security techniques, including cryptography and biometrics

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Published Papers (1 paper)

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Research

23 pages, 8186 KiB  
Article
IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
by Nidhi Kundu, Geeta Rani, Vijaypal Singh Dhaka, Kalpit Gupta, Siddaiah Chandra Nayak, Sahil Verma, Muhammad Fazal Ijaz and Marcin Woźniak
Sensors 2021, 21(16), 5386; https://doi.org/10.3390/s21165386 - 9 Aug 2021
Cited by 150 | Viewed by 9562
Abstract
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a [...] Read more.
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality. Full article
(This article belongs to the Special Issue Artificial Intelligence Systems Design for IoT Applications)
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