sensors-logo

Journal Browser

Journal Browser

Virtual and Augmented Sensing Techniques via Embedded ML Models for IoT Measurement Infrastructures

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

Deadline for manuscript submissions: 25 November 2025 | Viewed by 908

Special Issue Editors


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

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 Issue Information

Dear Colleagues,

In recent years, we have witnessed the exponential growth of virtual and augmented sensing techniques, and of embedded machine learning (ML) as well. Virtual sensing encompasses all those methods using to estimate parameters that cannot be directly measured due to the unavailability of dedicated sensors, or due to a general unfeasibility (e.g., logistical constraints, expensiveness of infrastructures, etc.). On the other hand, augmented sensing includes all those techniques in which the performance of standard sensors, or of standard measuring systems, are enhanced, thus obtaining finer results with reduced associated uncertainties (e.g., augmenting a cheap and coarse sensor, thus avoiding the use of an expensive alternative). Both approaches are usually implemented by resorting to ML, and artificial intelligence (AI) in general, models applied to the gathered data. When such a step is directly performed on the sensor node, the recent paradigm of embedded ML is encountered, which has proven to be valid despite the limit imposed by the hardware platforms using simple, yet effective, ML models; otherwise, it is remotely accomplished on data collection centers, thus enabling to develop more sophisticated ML/AI models. The application scenarios are countless, especially whenever Internet of Things (IoT) measurement infrastructures are exploited: from environmental monitoring to smart cities, distributed and pervasive measurement infrastructures in critical environments, and industrial monitoring, etc.

This Special Issue aims to collect original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of virtual and augmented sensing techniques, as well as of embedded ML.

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

  • Virtual sensing;
  • Augmented sensing;
  • Embedded ML;
  • IoT measurement infrastructures;
  • Distributed and pervasive measurement systems;
  • Wireless sensor networks;
  • Environmental monitoring in a broad sense with virtual and augmented sensing.

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 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

32 pages, 5164 KiB  
Article
Decentralized Distributed Sequential Neural Networks Inference on Low-Power Microcontrollers in Wireless Sensor Networks: A Predictive Maintenance Case Study
by Yernazar Bolat, Iain Murray, Yifei Ren and Nasim Ferdosian
Sensors 2025, 25(15), 4595; https://doi.org/10.3390/s25154595 - 24 Jul 2025
Viewed by 335
Abstract
The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional [...] Read more.
The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article
Show Figures

Figure 1

Back to TopTop