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Edge Artificial Intelligence and Data Science for IoT-Enabled Systems

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 620

Special Issue Editors


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Guest Editor
School of Computing, Engineering and the Build Environment, University of Roehampton, London SW15 5PH, UK
Interests: data processing; applied artificial intelligence; mathematical modelling; soft computing; feature engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computing, School of Arts Humanities and Social Sciences, University of Roehampton, London SW15 5PJ, UK
Interests: artificial intelligence; cybersecurity; smart and connected healthcare; disability-focused technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital transformation of industry and society is being fueled by a powerful synergy of the Internet of Things (IoT) and edge computing, enabled data streams, and the analytical prowess of modern Data Science. This is no longer a niche technological trend but a fundamental shift in how organizations derive value, optimize operations, and tackle complex global challenges. The impending data surge presents a dual imperative: it demands not only more robust digital infrastructure but also, and more critically, more intelligent and autonomous analytical methodologies. Fortunately, the inherently data-centric nature of edge networks makes them a perfect candidate for Data Science techniques.

By formally integrating the structured lifecycle of Data Science for edge computing, ensuring reproducible scenarios and optimizing the collection, analysis, and visualization of sensor data, a transformative potential can be unlocked. This integration of edge artificial intelligence and data analytics is pivotal, allowing IoT devices to move beyond simple data collection to intelligent pattern recognition and autonomous decision-making at the edge of the network. As the IoT continues its trajectory as a primary engine of global data generation, the role of Data Science will only become more integral. The foremost challenge and opportunity for researchers and practitioners alike lies in seamlessly weaving these two fields together.

The objective of this Special Issue is to bring together original research as well as review articles discussing advanced AI, edge computing, and data preprocessing approaches for IoT-enabled decision-making or proposing a purely mathematical concept for improving the reliability and accuracy of data-driven IoT sensor systems in any form. We also welcome submissions that perform comprehensive theoretical analyses of existing solutions by connecting the ideas and techniques of intelligent edge computing with other disciplines. Review articles focusing on state-of-the-art solutions are also encouraged.

Potential topics to be covered:

Edge computing and Lightweight AI
Data accuracy and IoT sensor system reliability
Data fusion, processing, analysis, and classification
Sensing and imaging for reliable decision making
AI for decision making
Sensor calibration for reliable analysis
IoT in data collection and processing
Data compaction techniques
Edge computing
Mathematical and statistical methods for data analysis and edge computing

Dr. Mohammad Farhan Khan
Dr. Mamoona Humayun
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

  • artificial intelligence (AI)
  • internet of things (IoT)
  • edge computing
  • data processing
  • sensor data

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Published Papers (1 paper)

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Research

20 pages, 1116 KB  
Article
Edge-Enabled Hybrid Encryption Framework for Secure Health Information Exchange in IoT-Based Smart Healthcare Systems
by Norjihan Abdul Ghani, Bintang Annisa Bagustari, Muneer Ahmad, Herman Tolle and Diva Kurnianingtyas
Sensors 2025, 25(24), 7583; https://doi.org/10.3390/s25247583 - 14 Dec 2025
Viewed by 252
Abstract
The integration of the Internet of Things (IoT) and edge computing is transforming healthcare by enabling real-time acquisition, processing, and exchange of sensitive patient data close to the data source. However, the distributed nature of IoT-enabled smart healthcare systems exposes them to severe [...] Read more.
The integration of the Internet of Things (IoT) and edge computing is transforming healthcare by enabling real-time acquisition, processing, and exchange of sensitive patient data close to the data source. However, the distributed nature of IoT-enabled smart healthcare systems exposes them to severe security and privacy risks during health information exchange (HIE). This study proposes an edge-enabled hybrid encryption framework that combines elliptic curve cryptography (ECC), HMAC-SHA256, and the Advanced Encryption Standard (AES) to ensure data confidentiality, integrity, and efficient computation in healthcare communication networks. The proposed model minimizes latency and reduces cloud dependency by executing encryption and verification at the network edge. It provides the first systematic comparison of hybrid encryption configurations for edge-based HIE, evaluating CPU usage, memory consumption, and scalability across varying data volumes. Experimental results demonstrate that the ECC + HMAC-SHA256 + AES configuration achieves high encryption efficiency and strong resistance to attacks while maintaining lightweight processing suitable for edge devices. This approach provides a scalable and secure solution for protecting sensitive health data in next-generation IoT-enabled smart healthcare systems. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence and Data Science for IoT-Enabled Systems)
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