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Electronics
Special Issues
Advanced Edge Intelligence in Smart Environments
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Advanced Edge Intelligence in Smart Environments

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 126

Special Issue Editor

Dr. Siew-Kei Lam

E-Mail Website
Guest Editor
College of Computing and Data Science, Nanyang Technological University, Singapore 639798, Singapore
Interests: edge intelligence; embedded vision for edge computing; secure and reliable embedded systems; analytics for smart urban mobility; heterogeneous and reconfigurable computing systems

Special Issue Information

Dear Colleagues,

The rapid proliferation of smart environments, encompassing domains such as smart cities, healthcare, transportation, and industrial automation, has driven the demand for efficient and real-time data processing. Edge intelligence—the convergence of edge computing and artificial intelligence (AI)—has emerged as a transformative paradigm, enabling localized data analytics and decision making at the network edge. This Special Issue focuses on “Advanced Edge Intelligence in Smart Environments”, highlighting the latest advancements, challenges, and applications in this dynamic field.

The integration of AI at the edge is reshaping the architecture of smart environments, offering solutions to issues such as latency, bandwidth constraints, energy efficiency, and privacy. Topics of interest include, but are not limited to, novel algorithms and frameworks for edge intelligence, federated and distributed learning approaches, resource allocation and optimization strategies, security- and privacy-enhancing techniques, and application-driven innovations in smart environments. Additionally, this Special Issue explores the synergy between edge intelligence and emerging technologies, such as the IoT and blockchain, which are poised to redefine the capabilities of edge systems.

By presenting cutting-edge research and real-world implementations, this Special Issue aims to provide a comprehensive overview of the state of the art in advanced edge intelligence, fostering interdisciplinary collaboration and guiding future developments in creating smarter, more resilient, and efficient environments. Researchers, practitioners, and policymakers are invited to contribute their insights and innovations to this critical and rapidly evolving field.

Dr. Siew-Kei Lam
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. Electronics 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 2400 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

  • edge intelligence
  • internet of things
  • resource allocation and optimization
  • real-time and energy efficiency
  • security and resiliency
  • federated and distributed learning
  • applications in smart cities, healthcare, transportation, and industrial automation

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Published Papers (1 paper)

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Research

16 pages, 1143 KiB  
Article
AlleyFloodNet: A Ground-Level Image Dataset for Rapid Flood Detection in Economically and Flood-Vulnerable Areas
by Ook Lee and Hanseon Joo
Electronics 2025, 14(10), 2082; https://doi.org/10.3390/electronics14102082 - 21 May 2025
Viewed by 11
Abstract
Urban flooding in economically and environmentally vulnerable areas—such as alleyways, lowlands, and semi-basement residences—poses serious threats. Previous studies on flood detection have largely relied on aerial or satellite-based imagery. While some studies used ground-level images, datasets capturing localized flooding in economically vulnerable urban [...] Read more.
Urban flooding in economically and environmentally vulnerable areas—such as alleyways, lowlands, and semi-basement residences—poses serious threats. Previous studies on flood detection have largely relied on aerial or satellite-based imagery. While some studies used ground-level images, datasets capturing localized flooding in economically vulnerable urban areas remain limited. To address this, we constructed AlleyFloodNet, a dataset designed for rapid flood detection in flood-vulnerable urban areas, with ground-level images collected from diverse regions worldwide. In particular, this dataset includes data from flood-vulnerable urban areas under diverse realistic conditions, such as varying water levels, colors, and lighting. By fine-tuning several deep learning models on AlleyFloodNet, the ConvNeXt-Large model achieved excellent performance, with an accuracy of 96.56%, precision of 95.45%, recall of 97.67%, and an F1 score of 96.55%. Comparative experiments with existing ground-level image datasets confirmed that datasets specifically designed for economically and flood-vulnerable urban areas, like AlleyFloodNet, are more effective for detecting floods in these regions. By successfully fine-tuning deep learning models, AlleyFloodNet not only addresses the limitations of existing flood monitoring datasets but also provides foundational resources for developing practical, real-time flood detection and alert systems for urban populations vulnerable to flooding. Full article
(This article belongs to the Special Issue Advanced Edge Intelligence in Smart Environments)
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