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Artificial Intelligence (AI)-Based Approaches for Developing Low-Cost Sensor (LCS) IoT Systems

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

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 15534

Special Issue Editors

Dr. Dhaval Thakker

E-Mail Website
Guest Editor
School of Computing, University of Bradford, Bradford LS2 9JT, UK
Interests: Internet of Things(IoT); citizen science; explainable AI; data science; environment
Special Issues, Collections and Topics in MDPI journals
Dr. Bhupesh Kumar Mishra

E-Mail Website
Co-Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: data science and machine learning; citizen science and smart cities; scheduling and optimisation and IoT

Special Issue Information

Dear Colleagues,

With the emergence of low-cost sensors (LCS), monitoring in real-time on a mass scale has become more feasible as the LCS provide new opportunities to supplement the existing high-cost devices. With opportunities for scaling up real-time monitoring with LCS comes challenges of accuracy, quality and reliability of the data collected by these devices. Recently, different approaches and Artificial Intelligence (AI)-based techniques have been emerging to ascertain confidence in data collected by LCS. However, both this approach and experience with dealing with such data are still in their infancy.

This Special Issue welcomes original research papers focusing on Artificial Intelligence (AI) techniques for improving the accuracy, quality and reliability of the data collected from LCS. In particular, we invite manuscripts in the following areas.

  1. Data accuracy in IoT systems;
  2. Data reliability in IoT systems;
  3. Data quality in IoT systems;
  4. Data standerization in heterogenous IoT systems;
  5. IoT data streaming.

We also welcome papers with interdisciplinary approaches considering but not limited to the following IoT application domains:

  1. IoT-based agriculture;
  2. Climate and environment;
  3. IoT and health;
  4. Air quality monitoring;
  5. Transporation system;
  6. Security and surveillance;
  7. Sensor networks.

Dr. Dhaval Thakker

Dr. Bhupesh Kumar Mishra
Guest Editor

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 (5 papers)

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Research

16 pages, 2740 KiB  
Article
FedMSA: A Model Selection and Adaptation System for Federated Learning
by Rui Sun, Yinhao Li, Tejal Shah, Ringo W. H. Sham, Tomasz Szydlo, Bin Qian, Dhaval Thakker and Rajiv Ranjan
Sensors 2022, 22(19), 7244; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197244 - 24 Sep 2022
Cited by 3 | Viewed by 1792
Abstract
Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we [...] Read more.
Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers’ expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano). Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Approaches for Developing Low-Cost Sensor (LCS) IoT Systems)
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19 pages, 6744 KiB  
Article
Internet of Things: Development Intelligent Programmable IoT Controller for Emerging Industry Applications
by Ti-An Chen, Shu-Chuan Chen, William Tang and Bo-Tsang Chen
Sensors 2022, 22(14), 5138; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145138 - 08 Jul 2022
Cited by 5 | Viewed by 2427
Abstract
The Internet of Things (IoT) has become critical to the implementation of Industry 4.0. The successful operation of smart manufacturing depends on the ability to connect everything together. In this research, we applied the TOC (Theory of Constraints) to develop a wireless Wi-Fi [...] Read more.
The Internet of Things (IoT) has become critical to the implementation of Industry 4.0. The successful operation of smart manufacturing depends on the ability to connect everything together. In this research, we applied the TOC (Theory of Constraints) to develop a wireless Wi-Fi intelligent programmable IoT controller that can be connected to and easily control PLCs. By applying the TOC-focused thinking steps to break through their original limitations, the development process guides the user to use the powerful and simple flow language process control syntax to efficiently connect to PLCs and realize the full range of IoT applications. Finally, this research uses oil–water mixer equipment as the target of continuous improvement and verification. The verification results meet the requirements of the default function. The IoT controller developed in this research uses a marine boiler to illustrate the application. The successful development of flow control language by TOC in this research will enable academic research on PLC-derivative applications. The results of this research will help more SMEs to move into smart manufacturing and the new realm of Industry 4.0. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Approaches for Developing Low-Cost Sensor (LCS) IoT Systems)
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21 pages, 688 KiB  
Article
Securing Fog Computing with a Decentralised User Authentication Approach Based on Blockchain
by Otuekong Umoren, Raman Singh, Zeeshan Pervez and Keshav Dahal
Sensors 2022, 22(10), 3956; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103956 - 23 May 2022
Cited by 13 | Viewed by 3781
Abstract
The use of low-cost sensors in IoT over high-cost devices has been considered less expensive. However, these low-cost sensors have their own limitations such as the accuracy, quality, and reliability of the data collected. Fog computing offers solutions to those limitations; nevertheless, owning [...] Read more.
The use of low-cost sensors in IoT over high-cost devices has been considered less expensive. However, these low-cost sensors have their own limitations such as the accuracy, quality, and reliability of the data collected. Fog computing offers solutions to those limitations; nevertheless, owning to its intrinsic distributed architecture, it faces challenges in the form of security of fog devices, secure authentication and privacy. Blockchain technology has been utilised to offer solutions for the authentication and security challenges in fog systems. This paper proposes an authentication system that utilises the characteristics and advantages of blockchain and smart contracts to authenticate users securely. The implemented system uses the email address, username, Ethereum address, password and data from a biometric reader to register and authenticate users. Experiments showed that the proposed method is secure and achieved performance improvement when compared to existing methods. The comparison of results with state-of-the-art showed that the proposed authentication system consumed up to 30% fewer resources in transaction and execution cost; however, there was an increase of up to 30% in miner fees. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Approaches for Developing Low-Cost Sensor (LCS) IoT Systems)
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11 pages, 451 KiB  
Article
Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
by Fawzy Habeeb, Tomasz Szydlo, Lukasz Kowalski, Ayman Noor, Dhaval Thakker, Graham Morgan and Rajiv Ranjan
Sensors 2022, 22(6), 2375; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062375 - 19 Mar 2022
Cited by 2 | Viewed by 1786
Abstract
Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data [...] Read more.
Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Approaches for Developing Low-Cost Sensor (LCS) IoT Systems)
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23 pages, 13334 KiB  
Article
Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring
by Rameez Raja Kureshi, Bhupesh Kumar Mishra, Dhavalkumar Thakker, Reena John, Adrian Walker, Sydney Simpson, Neel Thakkar and Agot Kirsten Wante
Sensors 2022, 22(3), 1093; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031093 - 31 Jan 2022
Cited by 17 | Viewed by 4711
Abstract
With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection [...] Read more.
With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Approaches for Developing Low-Cost Sensor (LCS) IoT Systems)
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