IoT, Edge Computing and AI: Enabling Emerging Intelligent Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 25804

Special Issue Editors


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Guest Editor
Department of Computing, College of Business, Technology and Engineering, Sheffield Hallam University, City Campus, Howard Street, Sheffield S1 1WB, UK
Interests: mobile data management; ambient intelligence; context awareness; activity recognition; service personalization; IoT trust management; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
University of Plymouth
Interests: eHealth; digital health; data science; artificial intelligence
Multimedia Communication and Intelligent Control, School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
Interests: prediction and control of video quality using AI, ML, cloud computing, fuzzy logic, applying computer vision techniques, and deep learning in pedestrian recognition; disease identification in cotton crops and damage recognition in wind turbines
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, University of Salford, Salford M5 4WT, UK
Interests: digital health; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the critical factors for digital healthcare transformation is the proliferation of Internet of Things (IoT) devices to support healthcare and wellbeing. These devices allow connected and remote management of citizens’ healthy living and wellbeing, particularly as occasioned by the current coronavirus pandemic. IoT devices, i.e., wearable devices, from fitness trackers to portable blood pressure and insulin monitors, etc., generate an incredible amount of data, which could be analyzed to make time-critical and delay-sensitive decisions. However, current solutions rely on data processing and management in the cloud, which does not support time-critical decision making. Therefore, emerging and disruptive technologies such as edge computing, artificial intelligence (AI) and machine learning (ML) have the potential to address the challenges of citizens’ health and wellbeing using IoT and edge devices. With these technologies, data can be processed at the network edge, closer to the citizens, thereby allowing critical data to be collected and processed in real time, arming healthcare givers with the essential knowledge to save lives.

In this Special Issue, the aim is to publish high-quality articles including reviews that address various challenges in the use of these technologies (AI, IoT, edge computing) to support the healthcare and wellbeing of citizens.

The topics of interest include but not limited to:

  • Complex physical activity monitoring using IoT devices and AI;
  • AI techniques for health and wellbeing monitoring and prediction using IoT devices;
  • Security and privacy preservation of citizen sensitive data;
  • mHealth sensing and apps in healthcare;
  • Edge intelligence for healthcare management;
  • Real time and context-aware wellbeing/activity monitoring using IoT devices;
  • Edge computing for healthcare and citizens’ wellbeing management;
  • Ambient intelligence for homecare management using AI and edge computing;
  • Embedded AI for healthcare and wellbeing management;
  • AI powered Edge devices for physical activity monitoring, such as sport activity monitoring;
  • Mobile and ambient assisted living in smart home environment;
  • Senior citizens activity monitoring;
  • Medical image analysis;
  • Supporting citizens with disabilities using edge devices and AI, e.g., dangerous situation recognition, best route recommendation for blind pedestrians;
  • Security, privacy and trust in IoT for citizen’s healthcare and wellbeing;
  • Other emerging applications of AI, Edge computing and IoT.

Dr. Abayomi Otebolaku
Prof. Gyu Myoung Lee
Prof. Edward Meinert
Dr. Asiya Khan
Dr. Gloria Iyawa
Guest Editors

Manuscript Submission Information

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Keywords

  • Complex physical activity monitoring using IoT devices and AI
  • AI techniques for health and wellbeing monitoring and prediction using IoT devices
  • Security and privacy preservation of citizen sensitive data
  • mHealth sensing and apps in healthcare
  • Edge intelligence for healthcare management
  • Real time and context-aware wellbeing/activity monitoring using IoT devices
  • Edge computing for healthcare and citizens’ wellbeing management
  • Ambient intelligence for homecare management using AI and edge computing
  • Embedded AI for healthcare and wellbeing management
  • AI powered Edge devices for physical activity monitoring, such as sport activity monitoring
  • Mobile and ambient assisted living in smart home environment
  • Senior citizens activity monitoring
  • Medical image analysis
  • Supporting citizens with disabilities using edge devices and AI, e.g., dangerous situation recognition, best route recommendation for blind pedestrians
  • Security, privacy and trust in IoT for citizen’s healthcare and wellbeing
  • Other emerging applications of AI, Edge computing and IoT

Published Papers (4 papers)

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Research

13 pages, 998 KiB  
Article
Assessing Artificial Intelligence Technology Acceptance in Managerial Accounting
by Anca Antoaneta Vărzaru
Electronics 2022, 11(14), 2256; https://doi.org/10.3390/electronics11142256 - 19 Jul 2022
Cited by 21 | Viewed by 9335
Abstract
The increasing expansion of digital technologies has significantly changed most economic activities and professions. As a result of the scientific and technological revolution 4.0, organizational structures and business models have changed, and new ones have emerged. Consequently, the accounting activities that record operations [...] Read more.
The increasing expansion of digital technologies has significantly changed most economic activities and professions. As a result of the scientific and technological revolution 4.0, organizational structures and business models have changed, and new ones have emerged. Consequently, the accounting activities that record operations and provide the necessary information to managers for decision making have faced threats, challenges, and opportunities, which have changed and will change the DNA of managerial accounting, determining a reinventing of it. As a result of the evolution of data collection and processing technologies, managerial accounting activities have become increasingly complex, encompassing increasing volumes of data. Resistance to change, organizational culture, lack of trust, and the high price of technology are the most critical barriers that interfere with adopting artificial intelligence technology in managerial accounting. This study aimed to assess the acceptance of artificial intelligence technology among accountants in Romanian organizations in the context of the modernization and digitization of managerial accounting. This research was quantitative, carried out through a survey based on a questionnaire. In total, 396 specialists in managerial accounting from Romanian organizations filled and returned the questionnaire. Using structural equation modeling, we tested the model of accepting artificial intelligence technology in managerial accounting. The results show that implementing artificial intelligence solutions in managerial accounting offers multiple options to managers through innovation and shortening processes, improves the use of accounting information, and is relatively easy to use, given the high degree of automation and customization. Full article
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14 pages, 1432 KiB  
Article
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits
by Poonam Dhiman, Vinay Kukreja, Poongodi Manoharan, Amandeep Kaur, M. M. Kamruzzaman, Imed Ben Dhaou and Celestine Iwendi
Electronics 2022, 11(3), 495; https://doi.org/10.3390/electronics11030495 - 8 Feb 2022
Cited by 161 | Viewed by 6067
Abstract
Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by [...] Read more.
Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity. Full article
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19 pages, 3157 KiB  
Article
Multilink Internet-of-Things Sensor Communication Based on Bluetooth Low Energy Considering Scalability
by Dong-Suk Ryu, Yeung-Mo Yeon and Seung-Hee Kim
Electronics 2021, 10(19), 2335; https://doi.org/10.3390/electronics10192335 - 23 Sep 2021
Cited by 3 | Viewed by 3094
Abstract
As the growth rate of the internet-of-things (IoT) sensor market is expected to exceed 30%, a technology that can easily collect and processing a large number of various types of sensor data is gradually required. However, conventional multilink IoT sensor communication based on [...] Read more.
As the growth rate of the internet-of-things (IoT) sensor market is expected to exceed 30%, a technology that can easily collect and processing a large number of various types of sensor data is gradually required. However, conventional multilink IoT sensor communication based on Bluetooth low energy (BLE) enables only the processing of up to 19 peripheral nodes per central device. This study suggested an alternative to increasing the number of IoT sensor nodes while minimizing the addition of a central processor by expanding the number of peripheral nodes that can be processed per central device through a new group-switching algorithm based on Bluetooth low energy (BLE). Furthermore, this involves verifying the relevancy of application to the industry field. This device environment lowered the possibility of data errors and equipment troubles due to communication interference between central processors, which is a critical advantage when applying it to industry. The scalability and various benefits of a group-switching algorithm are expected to help accelerate various services via the application of BLE 5 wireless communication by innovatively improving the constraint of accessing up to 19 nodes per central device in the conventional multilink IoT sensor communication. Full article
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27 pages, 1051 KiB  
Article
Security of Things Intrusion Detection System for Smart Healthcare
by Celestine Iwendi, Joseph Henry Anajemba, Cresantus Biamba and Desire Ngabo
Electronics 2021, 10(12), 1375; https://doi.org/10.3390/electronics10121375 - 8 Jun 2021
Cited by 45 | Viewed by 4696
Abstract
Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the [...] Read more.
Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%. Full article
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