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Multimodal Sensing Technologies for IoT and AI-Enabled Systems

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 1526

Special Issue Editors


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Guest Editor
School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: cyber-physical systems; Internet of Things; autonomous systems; AI for robotics; autonomous cars
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Laboratory of Electronic Media, School of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: media technologies; audiovisual capturing; audiovisual signal processing; machine learning; multimedia semantics; cross-media authentication; digital audio and audiovisual forensics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: software engineering processes; model-driven engineering; software quality and software analytics; middleware robotics and knowledge extraction from big data repositories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce this Special Issue, entitled "Multimodal Sensing Technologies for IoT and AI-Enabled Systems", in the renowned international journal Sensors.

In today's world, multimodal data and sensing technologies have emerged as crucial components within the Internet of Things (IoT) and artificial intelligence (AI) paradigms, influencing multiple fields, from healthcare to industry, media, education, robotics, transportation, and environmental monitoring, shaping broader multidisciplinary research and application projects. Due to time, location and contextual awareness, integrating IoT with AI has led to enhanced smart systems capable of performing complex tasks autonomously, thereby contributing to the development of intelligent societies. This Special Issue aims to bring together cutting-edge research and the latest advancements in multimodal sensing technologies, IoT, and AI-enabled systems, combining imaging applications, audiovisual reaction monitoring, and broader sensing technologies (e.g., temperature, humidity, air pollution, interaction recording, etc.), thus forming multimodal fusion decision systems. The proposed Special Issue is an excellent match to the objectives of Sensors, in addition to aligning itself perfectly with the journal’s multidisciplinary nature.

We encourage the submission of high-quality papers demonstrating these technologies' potential to shape our future, drive innovation, and offer solutions to real-world problems. Authors are invited to submit original research works, viewpoint articles, case studies, reviews, theoretical, and critical perspectives.

Topics of interest may include, but are not limited to, the following:

  • Design and implementation of multimodal sensors for IoT.
  • AI techniques for multimodal sensor data analysis.
  • Integration of AI and IoT for smart system development.
  • Security and privacy in AI-enabled IoT systems.
  • Real-world applications and case studies of multimodal sensing technologies in IoT and AI-enabled systems.
  • Data analytics and intelligent content management systems.
  • Multimodal sensing and fused decision-making in robotics.
  • Data journalism/visualization and media automations using multimodal sensing with AI-enabled systems.
  • Environmental data-driven monitoring automations.
  • Educational and digital literacy applications of IoT and AI-enabled systems.
  • Biomedical engineering applications of IoT and AI-enabled systems.
  • Multimodal sensing for data crowdsourcing and datasets organization.
  • Sensing technology for cyber–physical systems.
  • Sensor technology for agile data retrieval and analytics.
  • Sensing technology for AI-enabled systems.
  • Adaptive/modular sensor technology for data management.
  • Model-driven engineering approaches for multimodal sensor systems.

Dr. Emmanouil Tsardoulias
Prof. Dr. Charalampos Dimoulas
Prof. Dr. Andreas L. Symeonidis
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

  • artificial intelligence
  • AI-enabled systems
  • data-driven systems
  • Internet of Things
  • machine learning
  • multimodal decision making
  • multimodal sensing
  • smart systems

Published Papers (1 paper)

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Research

18 pages, 3646 KiB  
Article
Multimodal Environmental Sensing Using AI & IoT Solutions: A Cognitive Sound Analysis Perspective
by Alexandros Emvoliadis, Nikolaos Vryzas, Marina-Eirini Stamatiadou, Lazaros Vrysis and Charalampos Dimoulas
Sensors 2024, 24(9), 2755; https://doi.org/10.3390/s24092755 - 26 Apr 2024
Viewed by 206
Abstract
This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate [...] Read more.
This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate efficient data transfer and storage. This is accomplished without undermining the accuracy necessary for trustworthy air pollution analysis while simultaneously minimizing processing expenses. More specifically, our approach fuses a Deep-Learning-based model, optimized for edge devices, along with a conventional coding schema for audio compression. Once transmitted to the cloud, the compressed data undergo a decoding process, leveraging vast cloud computing resources for accurate reconstruction and classification. The experimental results indicate that our approach leads to a relatively minor decrease in accuracy, even at notably low bit rates, and demonstrates strong robustness in identifying data from labels not included in our training dataset. Full article
(This article belongs to the Special Issue Multimodal Sensing Technologies for IoT and AI-Enabled Systems)
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