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Smart Sensing and Embedded AI for IoT Systems

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Internet of Things".

Deadline for manuscript submissions: 25 April 2027 | Viewed by 353

Editors


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Guest Editor
Department of Information Engineering, University of Padova, 35131 Padova, Italy
Interests: internet of things; communications; low power wide area networks; wireless sensor networks; augmented and virtual sensing techniques; virtual instruments and measurements; embedded ML; embedded systems; distributed measurement systems; acoustic instrumentation and measurements; remote sensing applications; telecommunication measurements; artificial intelligence; optical instrumentation and measurements; light/optical/optics/optoelectronic sensors; environmental measurements; sensing strategy and methodology; sensor fusion; sensors and actuators; electrical/electronic sensors; sound/ultrasonic sensors; temperature and thermal sensors; data acquisition systems; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, 35131 Padova, Italy
Interests: internet of things; low power wide area networks; augmented and virtual sensing techniques; embedded ML; distributed measurement systems; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in sensing technologies and embedded Artificial Intelligence (AI) are revolutionizing the way data is acquired, processed, and utilized in Internet of Things (IoT) systems. By integrating smart sensing techniques with embedded AI, we can now achieve real-time, adaptive, and context-aware monitoring solutions across various application domains, including industrial automation, environmental monitoring, smart cities, healthcare, and infrastructure management. This Special Issue aims to bring together high-quality research on the design, development, and deployment of smart sensing systems powered by embedded AI. We particularly focused on solutions that effectively combine sensing, computation, and communication capabilities at the edge, enabling efficient and scalable IoT systems.

We welcome original research articles and reviews that explore theoretical advancements, methodological developments, and practical implementations of smart sensing and embedded AI in IoT contexts. Contributions that demonstrate real-world applications, experimental validation, and system-level integration are especially encouraged.

Potential topics include, but are not limited to, the following:

  • Smart sensing techniques and adaptive sensing systems;
  • Embedded AI and edge AI;
  • Machine learning and deep learning for embedded systems;
  • TinyML and resource-constrained AI;
  • Sensor fusion and multimodal data processing;
  • Virtual sensing and augmented sensing techniques;
  • Distributed and collaborative sensing in IoT;
  • Real-time data processing and edge analytics;
  • Energy-efficient and low-power sensing systems;
  • Wireless sensor networks and IoT architectures;
  • Intelligent monitoring and predictive maintenance;
  • Smart sensing for industrial IoT applications;
  • Environmental and urban monitoring systems;
  • Healthcare and wearable sensing systems;
  • Security privacy and trust in smart sensing systems;
  • Hardware-software co-design for embedded AI;
  • Communication-efficient AI for IoT systems.

Dr. Giacomo Peruzzi
Dr. Alessandro Pozzebon
Prof. Dr. Matteo Bertocco
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • smart sensing techniques and adaptive sensing systems
  • embedded AI and edge AI
  • machine learning and deep learning for embedded systems
  • TinyML and resource-constrained AI
  • sensor fusion and multimodal data processing
  • virtual sensing and augmented sensing techniques
  • distributed and collaborative sensing in IoT
  • real-time data processing and edge analytics
  • energy-efficient and low-power sensing systems
  • wireless sensor networks and IoT architectures
  • intelligent monitoring and predictive maintenance
  • smart sensing for industrial IoT applications
  • environmental and urban monitoring systems
  • healthcare and wearable sensing systems
  • security privacy and trust in smart sensing systems
  • hardware-software co-design for embedded AI
  • communication-efficient AI for IoT systems

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Published Papers (1 paper)

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Research

19 pages, 3761 KB  
Article
Multi-Granular Embedding and Hybrid Encoder–Decoder Architecture with Temporal Linear Regression Bypass for Sensor-Based Time Series Forecasting in Smart Infrastructure
by Hsu-Yung Cheng, Huan-Hsuan Lin, Chi-Lun Jiang and Chih-Chang Yu
Sensors 2026, 26(18), 5953; https://doi.org/10.3390/s26185953 (registering DOI) - 20 Sep 2026
Abstract
In this work, we propose a robust deep encoder–decoder neural network that integrates multiple deep sequence models for time series forecasting in sensor-based applications. The proposed approach features a sophisticated input embedding layer that integrates scalar features, position encodings, and multi-granularity time embeddings. [...] Read more.
In this work, we propose a robust deep encoder–decoder neural network that integrates multiple deep sequence models for time series forecasting in sensor-based applications. The proposed approach features a sophisticated input embedding layer that integrates scalar features, position encodings, and multi-granularity time embeddings. To improve computational efficiency, we employ a generative-style decoder inspired by the Informer architecture, which enables full-length sequence prediction in a single iteration, avoiding the latency associated with traditional autoregressive models. The architecture is highly flexible, supporting either Gated Recurrent Unit (GRU) or Transformer blocks as foundational layers to process latent temporal dependencies. Recognizing that purely nonlinear deep learning models often struggle to capture sharp, short-term local variations due to inherent input scale insensitivity, we augment our model with a parallel temporal linear regression unit. This bypass mechanism captures linear local trends, effectively combining the predictive power of nonlinear deep feature extraction with the sensitivity of linear regression. Experimental evaluations on the Electricity Consumption (EC) and Speed Index California (SIC) datasets demonstrate that the GRU-based implementation reduces the average Mean Squared Error (MSE) by approximately 22% and 16%, respectively, compared with the best-performing baseline across different output lengths. Ablation studies further confirm that the temporal linear regression bypass substantially improves forecasting accuracy, particularly on the EC dataset, where it provides an average MSE reduction of approximately 55%. Full article
(This article belongs to the Special Issue Smart Sensing and Embedded AI for IoT Systems)
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