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Applied Sciences
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Smart Sensor Networks for Environmental Monitoring
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Smart Sensor Networks for Environmental Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 1454

Editors

Dr. Pau Ferrer-Cid

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Guest Editor
Computer Architecture Department, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: machine learning; graph signal processing; artificial intelligence of things; air pollution
Dr. Alessandro Lo Schiavo

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università degli Studi della Campania "Luigi Vanvitelli", Via Roma, 81031 Aversa, CE, Italy
Interests: analysis and design of analog circuits; RF communication circuits; nonlinear circuit theory; circuit simulation; wireless sensor networks and electronic circuits for energy harvesting
Special Issues, Collections and Topics in MDPI journals
Prof. Luigi Fortuna

E-Mail Website
Guest Editor
1. Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Universita degli Studi di Catania, Viale A. Doria 6, 95125 Catania, Italy
2. ST-Lab, 95126 Catania, Italy
Interests: non-linear dynamical systems; automatic control; autonomous system control; non-linear electronic circuits and devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances and the maturity of Internet of Things (IoT)-enabling technologies have allowed the adoption of sensor networks for various applications. Monitoring networks are currently used to examine different environmental parameters, such as temperature or air quality, to carry out tasks such as hotspot detection and forecasting, among others. With the rise of artificial intelligence (AI) and sensor data-processing techniques, sensor networks have been endowed with processing capabilities in smart sensors and smart sensor networks by improving the quality of sensed data through missing value imputation or outlier detection techniques, creating virtual sensors, using predictive or automated maintenance, and emerging new applications. Currently, the artificial Intelligence of Things (AIoT) focuses on the use of AI on data from IoT platforms such as monitoring sensor networks. In its pure state, the AIoT brings advanced processing techniques to the edge or devices, improving the processing capabilities of smart sensors. This Special Issue invites contributions featuring sensor network deployments for environmental monitoring, advanced sensor data-processing techniques, AIoT-based models, and more, including advanced techniques for sensor networks in environmental monitoring.

The recommended topics include, but are not limited to, the following:

  • AI-powered sensor network deployment for environmental monitoring;
  • Novel sensor data-processing techniques;
  • AIoT for environmental monitoring;
  • Data quality enhancement techniques;
  • IoT deployment for environmental monitoring;
  • Smart sensing techniques;
  • Autonomous and automatic sensor network tools in environmental scenarios.

Dr. Pau Ferrer-Cid
Dr. Alessandro Lo Schiavo
Prof. Dr. Luigi Fortuna
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. Applied Sciences 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

  • artificial intelligence of things
  • environmental monitoring
  • internet of things
  • sensor data processing
  • sensor network deployment
  • sensor networks
  • smart sensing
  • smart sensors

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Published Papers (1 paper)

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Research

31 pages, 5042 KB  
Article
VAS-DPFF: Virtual Augmented Sensor Based on Deterministic and Probabilistic Feature Fusion for Environmental Monitoring
by Muhammad Faizan, Qazi Waqas Khan, Murad Ali Khan, Syed Shehryar Ali Naqvi, Ji-Eun Kim, SeungMyeong Jeong, Il-yeop Ahn and Do Hyeun Kim
Appl. Sci. 2026, 16(14), 7141; https://doi.org/10.3390/app16147141 (registering DOI) - 16 Jul 2026
Abstract
Smart sensor networks for environmental monitoring require accurate and continuous estimation of key variables such as temperature, humidity, and wind speed; however, physical sensor deployments are frequently limited by high costs, hardware failures, and data quality degradation, while existing virtual sensor approaches rely [...] Read more.
Smart sensor networks for environmental monitoring require accurate and continuous estimation of key variables such as temperature, humidity, and wind speed; however, physical sensor deployments are frequently limited by high costs, hardware failures, and data quality degradation, while existing virtual sensor approaches rely on single-model architectures that lack explicit uncertainty modeling and fail to capture the complex non-linear dynamics of real-world IoT time-series data. This paper proposes VAS-DPFF, a virtual augmented sensor framework based on deterministic and probabilistic feature fusion, which contributes a principled integration of well-established deterministic and probabilistic techniques within a unified AIoT-compatible virtual sensing architecture. The framework integrates: (i) a deterministic pipeline comprising temporal encoding, rolling statistics, and mutual information-based feature selection; (ii) a probabilistic pipeline employing Bayesian Ridge Regression (BRR) and Gaussian Process Regression (GPR) to generate uncertainty-aware synthetic features; and (iii) an early feature-level fusion strategy feeding an XGBoost regression model augmented with Gaussian noise injection. Experiments on 84,582 time-series records from a nine-station IoT environmental monitoring network in Gwacheon City, South Korea, demonstrate strong multi-target prediction performance: temperature RMSE =0.811 ∘C, R2=0.973; humidity RMSE =4.113%, R2=0.964; and wind speed RMSE =0.602 m/s, R2=0.798, representing RMSE reductions of 61.2%, 60.7%, and 62.3% over the existing method, respectively. Comprehensive ablation studies, sensitivity analysis, and augmentation validation confirm that the proposed integration of deterministic and probabilistic features yields consistent and practically valuable improvements in virtual sensing performance suitable for AIoT-enabled smart sensor network deployments across multiple environmental monitoring targets. Full article
(This article belongs to the Special Issue Smart Sensor Networks for Environmental Monitoring)
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