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Intelligent Signal Processing and Sensing Technology 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: 20 December 2025 | Viewed by 802

Special Issue Editors


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Guest Editor
College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
Interests: machine learning; smart sensors; IoT; biomedical signal processing; biomedical image processing; smartphone healthcare applications (mHealth); IoT in agriculture

E-Mail Website
Guest Editor
College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
Interests: renewable energy; energy storage systems; machine learning; image/signal processing; internet of things (IoT); biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of internet of things (IoT) technology has ushered in a new era of intelligent systems that are transforming various industries. This Special Issue aims to highlight the latest research and developments in intelligent signal processing and sensing technologies for IoT applications. Topics of interest include, but are not limited to, IoT applications in healthcare, agriculture, smart cities, industrial automation, and environmental monitoring. We invite contributions that explore innovative sensor designs, advanced signal processing techniques, integration with AI and machine learning, and real-world case studies demonstrating the impact of IoT technologies.

  • IoT applications;
  • Intelligent signal processing;
  • Sensing technology;
  • Healthcare IoT;
  • Agricultural IoT;
  • Smart cities;
  • Industrial IoT;
  • Environmental monitoring;
  • AI and machine learning;
  • Real-time data analysis.

This Special Issue fits within the scope of Sensors by focusing on the development and application of intelligent signal processing and sensing technologies that enhance IoT applications across various fields, aligning with the journal’s emphasis on innovative sensing technologies and applications.

Dr. Fatemehsadat Tabei
Dr. Behnam Askarian
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.

Keywords

  • IoT applications
  • intelligent signal processing
  • sensing technology

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Published Papers (1 paper)

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Research

17 pages, 5768 KiB  
Article
An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology
by Behnam Askarian, Amin Askarian, Fatemehsadat Tabei and Jo Woon Chong
Sensors 2025, 25(5), 1316; https://doi.org/10.3390/s25051316 - 21 Feb 2025
Viewed by 481
Abstract
Keratoconus (KC) is a progressive eye disease and a major cause of vision impairment and blindness worldwide. Early diagnosis is crucial for effective management, yet conventional diagnostic methods rely on expensive and bulky imaging devices, limiting accessibility, especially in resource-constrained settings. This paper [...] Read more.
Keratoconus (KC) is a progressive eye disease and a major cause of vision impairment and blindness worldwide. Early diagnosis is crucial for effective management, yet conventional diagnostic methods rely on expensive and bulky imaging devices, limiting accessibility, especially in resource-constrained settings. This paper introduces a novel smartphone-based approach for the early detection of KC, leveraging screen-projected Placido disc patterns and an advanced image processing framework. Unlike traditional corneal topographers, our method utilizes a unique Placido disc projection technique and a machine learning-based classification model to analyze corneal irregularities with high precision. With a sensitivity of 96.08%, specificity of 97.96%, and overall accuracy of 97% on our dataset, the proposed system demonstrates exceptional diagnostic reliability. By transforming a standard smartphone into an effective screening tool, this innovation provides an affordable, portable, and user-friendly solution for early KC detection, bridging the gap in eye care accessibility and reducing the global burden of undiagnosed keratoconus. Full article
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