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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 3318

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

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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 (3 papers)

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Research

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18 pages, 1206 KB  
Article
Edge Driven Trust Aware Threat Detection for IoT Enabled Intelligent Transportation Systems
by Khulud Salem Alshudukhi, Mamoona Humayun, Aala Oqab Alsalem, Mohammad Farhan Khan and Khalid Haseeb
Sensors 2026, 26(4), 1108; https://doi.org/10.3390/s26041108 - 9 Feb 2026
Cited by 1 | Viewed by 534
Abstract
Wireless communication and the Internet of Things (IoT) are integrated for the formulation of an emerging Intelligent Transportation System (ITS) for the interaction of vehicles and to enhance road safety. The emerging network manages the traffic flow, real-time data analytics, and resource control [...] Read more.
Wireless communication and the Internet of Things (IoT) are integrated for the formulation of an emerging Intelligent Transportation System (ITS) for the interaction of vehicles and to enhance road safety. The emerging network manages the traffic flow, real-time data analytics, and resource control for the development of urban transportation systems and smart cities. Extensive research has been conducted on the development of efficient routing response time for the IoT-ITS environment; however, the rapid changes in the network topologies still lead to unmanageable congestion and communication holes. Moreover, it is also often threatened due to high urban mobility and incurs additional transmission with excessive overhead. Such concepts are not able to maintain secure interactions among vehicles and expose confidential data to malicious devices while interacting on unpredictable channels. This research proposes a trust-aware edge-assisted model to secure the vehicular network and offers a more reliable system with optimal routing performance. The global trust model is maintained based on network conditions using localized computing and attaining data privacy and coherence. Furthermore, a blockchain ledger is included along with trust to ensure tamper-proof and transparent computing across the boundaries of the IoT-ITS environment. The proposed model is compared with Graph-Based Trust-Enabled Routing (GBTR) and Bacteria for Aging Optimization Algorithm (BFOA), and the results revealed significant performance for network throughput by 50% and 62.5%, end-to-end delay by 33.3% and 37.5%, routing overhead by 34% and 38.7%, and false positive rate by 67.9% and 68.5% over the dynamic network infrastructure. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence and Data Science for IoT-Enabled Systems)
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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 895
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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Review

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39 pages, 3890 KB  
Review
Deep Reinforcement Learning for Sustainable Urban Mobility: A Bibliometric and Empirical Review
by Sharique Jamal, Farheen Siddiqui, M. Afshar Alam, Mohammad Ayman-Mursaleen, Sherin Zafar and Sameena Naaz
Sensors 2026, 26(2), 376; https://doi.org/10.3390/s26020376 - 6 Jan 2026
Viewed by 1048
Abstract
This paper provides an empirical basis for a Computational Integration Framework (CIF), a systematic and scientifically supported implementation of artificial intelligence (AI) in smart city applications. This study is a methodological framework-with-validation study, where large-scale bibliometric analysis is used as a justification for [...] Read more.
This paper provides an empirical basis for a Computational Integration Framework (CIF), a systematic and scientifically supported implementation of artificial intelligence (AI) in smart city applications. This study is a methodological framework-with-validation study, where large-scale bibliometric analysis is used as a justification for design in the identification of strategically relevant urban areas rather than a single research study. This evidence determines urban mobility as the most mature and computationally optimal domain for empirical verification. The exploitation of CIF is realized using a DRL-driven traffic signal control system to show that bibliometrically informed domain selection can be put into application by way of an algorithm. The empirical results show that the most traditional control strategies accomplish significant performance gains, such as about 48% reduction in average wait time, over 30% increase in traffic efficiency, and considerable reductions in fuel consumption and CO2 emissions. A federated DRL solution maintains around 96% of central performance while still maintaining data privacy, which suggests that deployment in real-world situations is feasible. The contribution of this study is threefold: evidence-based domain selection through bibliometric analyses; introduction of CIF as an AI decision support bridge between AI techniques and urban application domains; and computational verification of the feasibility of DRL for sustainable urban mobility. These findings reveal policy information relevant to goals governing global sustainability, including the European Green Deal (EGD) and the United Nations Sustainable Development Goals (SDGs), and thus, the paper is a methodological framework paper based on literature and validated through computational experimentation. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence and Data Science for IoT-Enabled Systems)
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