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Intelligent Sensing Systems: From Design to IoT Integration

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 997

Special Issue Editors


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Guest Editor
Polytechnic Institute of Setúbal, Setúbal School of Technology, 2910-761 Setúbal, Portugal
Interests: industrial instrumentation; smart sensing; Industry 4.0; environmental monitoring
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. CINAV–Escola Naval, Base Naval de Lisboa, 2810-001 Almada, Portugal
2. Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
Interests: instrumentation; measurement; industrial protocols; smart sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of sensing technology has transitioned from simple transducers to sophisticated intelligent sensing systems (ISSs). Historically, sensors served as passive data collectors. However, the confluence of microelectronics, low-power processing, and the proliferation of the Internet of Things (IoT) has catalyzed a paradigm shift. Modern sensors are increasingly endowed with embedded intelligence, enabling on-chip signal processing, data analytics, and decision-making capabilities, transforming them into the autonomous perceptual backbone of our connected world.

This Special Issue aims to provide a comprehensive forum for the latest advancements that bridge the gap between fundamental sensor design and systemic IoT deployment. The scope of this issue is inherently interdisciplinary, encompassing a full range of technologies, from novel sensor materials and low-power electronics to the complex challenges of data management, interoperability, and security within distributed IoT architectures.

We invite the submission of contributions that push the boundaries of current knowledge. We are particularly interested in the integration of artificial intelligence (e.g., edge AI and TinyML), energy-harvesting techniques for autonomous operation, novel sensing methodologies, and secure communication protocols for massive sensor networks. Research on use cases and end-to-end maintenance is also welcome. We aim to solicit high-quality, original research articles and comprehensive review papers that have not been previously published.

Dr. José Miguel Costa Dias Pereira
Dr. Vítor Manuel Rodrigues Viegas
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-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

  • smart sensors
  • Internet of Things (IoT)
  • edge AI
  • energy harvesting
  • sensor networks
  • end-to-end device management

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Published Papers (1 paper)

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Research

20 pages, 5155 KB  
Article
Applying Monte Carlo Method for Straight-Line Model Sensor Calibration
by Pedro M. Ramos and Fernando M. Janeiro
Sensors 2026, 26(9), 2907; https://doi.org/10.3390/s26092907 - 6 May 2026
Viewed by 663
Abstract
Sensors are used in measurement systems to enable estimation of physical parameters. Their calibration is an essential requirement and to perform the overall system/sensor calibration, its input is changed while the output is measured. Parameters from an appropriate sensor model are then determined [...] Read more.
Sensors are used in measurement systems to enable estimation of physical parameters. Their calibration is an essential requirement and to perform the overall system/sensor calibration, its input is changed while the output is measured. Parameters from an appropriate sensor model are then determined from these measurements. If the model is a straight-line, a first-order least squares linear regression is commonly used to estimate the slope and offset—this is often called simple linear regression. However, this method is unable to consider uncertainty in the sensor/system input measurements. This paper reviews the possible methods to estimate the optimal straight-line parameters considering uncertainties in both input and output measurements. The Monte Carlo Method can deal with all types of uncertainties in each of the measurements, whether sensor inputs or outputs, and also take into account possible covariances of these measurements. A key aspect of this work is the application to the heteroscedastic case, where measurement uncertainties vary across observations. An MCM-based strategy is proposed to optimize the selection of new measurement input values to minimize the estimated slope uncertainty. This strategy is shown to significantly reduce, in the presented case, the number of required measurement values. Full article
(This article belongs to the Special Issue Intelligent Sensing Systems: From Design to IoT Integration)
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