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Innovative Applications and Strategies for IoT
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Innovative Applications and Strategies for IoT

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 5513

Special Issue Editor

Dr. Jianguo (Jack) Wang

E-Mail Website
Guest Editor
Transport Research Centre, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), 81 Broadway, Ultimo, NSW 2007, Australia
Interests: sensor fusion for surveying, navigation and perception; robotics and intelligent systems; environmentally friendly transportation and housing; GNSS, IMU, vision and laser sensors modelling and data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The IoT (Internet of Things) describes physical objects embedded with sensors and actuators that communicate with computing systems via the Internet to allow the physical world to be monitored or even controlled. IoT uses a variety of sensing technologies to connect the digital and physical worlds. The physical objects being monitored do not have to be manufactured, they can include objects in nature, such as environmental conditions, as well as people and animals. It has been applied to many applications, such as operations optimization, health, human productivity, and condition-based maintenance. As a booming field, many new strategies for IoT applications have been continuously developed. This Special Issue provides a platform for authors to present their new research development on this.

We encourage authors from academia and industry to submit new research results related to IoT applications and strategies. The topics include but are not limited to the following:

  • IoT in healthcare for telemedicine and remote healthcare;
  • IoT insecurity for preventing hackers;
  • IoT in business and service;
  • IoT for resilient organizations;
  • IoT in cities and transportation;
  • Industrial IoT (IIoT);
  • IoT in scientific research;
  • Sensing technologies in IoT.

Dr. Jianguo (Jack) Wang
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.

Keywords

  • IoT strategies
  • IoT applications
  • sensing in IoT
  • industrial IoT, IoT security
  • IoT system design and development

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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16 pages, 5576 KiB  
Article
An Optimal Internet of Things-Driven Intelligent Decision-Making System for Real-Time Fishpond Water Quality Monitoring and Species Survival
by Saima Kanwal, Muhammad Abdullah, Sahil Kumar, Saqib Arshad, Muhammad Shahroz, Dawei Zhang and Dileep Kumar
Sensors 2024, 24(23), 7842; https://doi.org/10.3390/s24237842 - 8 Dec 2024
Cited by 2 | Viewed by 2221
Abstract
Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance [...] Read more.
Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability. A custom-built dataset comprising 43,459 records, covering ten distinct fish species across diverse pond environments, was meticulously curated. The data were stored as a comma-separated values (CSV) file on the IoT cloud platform ThingSpeak and synchronized with Firebase, enabling seamless remote access, control, and real-time updates. Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. Our framework’s capability to manage complex, multi-species fishpond environments was validated across diverse setups, showcasing its potential to transform fish farming practices by ensuring sustainable climate-adaptive management through real-time water quality optimization. This study marks a significant step forward in climate-smart aquaculture, contributing to enhanced fish health, survival, and yield while mitigating the risks posed by climate change on aquatic ecosystems. Full article
(This article belongs to the Special Issue Innovative Applications and Strategies for IoT)
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24 pages, 8668 KiB  
Article
Mobile Application Development for Prepaid Water Meter Based on LC Sensor
by Ario Kusuma Purboyo, Hanif Fakhrurroja, Dita Pramesti and Achmad Rozan Chaidir
Sensors 2024, 24(20), 6762; https://doi.org/10.3390/s24206762 - 21 Oct 2024
Viewed by 2512
Abstract
This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in [...] Read more.
This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in monitoring water usage in Indonesia. Postpaid billing methods that rely on manual data recording are a source of concern due to potential inaccuracies caused by human error. This study presents the development of a prepaid water meter system that integrates LC sensors, BLE connectivity, a tokenization mechanism, and a mobile application to address this issue. The system offers a cost-effective solution by utilizing BLE + Global System for Mobile (GSM) from the user’s mobile phone. Using the design thinking methodology, the mobile application for the prepaid water meter achieved a usability testing score of 80. The load testing results for the back-end server, conducted with a sample size of 515 users, revealed a back-end latency of 1.973 milliseconds and an error rate of 8.74%. Furthermore, the LC sensors integrated into the PWM device showed an average error rate of 1.33%. The power consumption during each work cycle was measured at 129 mA and each battery is expected to last six years. Overall, with simple LC sensors, this system can precisely measure water usage. Full article
(This article belongs to the Special Issue Innovative Applications and Strategies for IoT)
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