sensors-logo

Journal Browser

Journal Browser

Signal Processing and Machine Learning for Sensor Systems (2nd Edition)

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1226

Special Issue Editors


E-Mail Website
Guest Editor
Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA
Interests: machine learning; data mining; signal processing; dynamical systems; chaos
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Electrical and Computer Engineering,Marquette University, Milwaukee, WI 53233, USA
Interests: biomedical data integration; artificial intelligence; big data analytics; machine learning; natural language processing; databases; distributed systems; information retrieval
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI 53233, USA
Interests: machine learning applied to optimization in multicore processors and datacenters; embedded systems; environment monitoring; and IoT security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue provides a platform for research on the processing of sensor signals, both at the edge and in large-scale data warehouses. Recent advances in machine learning and signal processing have driven significant progress in areas such as signal classification, fault detection, speech recognition, and industrial automation. With an estimated 14 billion Internet of Things (IoT) devices currently in use—ranging from smart lighting systems and cell phones to power tools and industrial equipment—the volume of sensor data being generated continues to expand exponentially. As this growth accelerates, efficient data processing closer to the device has become increasingly important. The integration of edge and embedded processing power, specialized signal processing hardware, and compact neural network architectures has enabled machine learning at the edge, resulting in intelligent devices capable of adapting to user needs. Additionally, processing sensor signals collected in data warehouses enables the application of sophisticated signal processing and deep machine learning algorithms. This Special Issue welcomes original research and review articles addressing the processing of sensor signals using machine learning techniques—both in edge and embedded environments and in data warehouse or cloud-based frameworks.

Dr. Richard J. J. Povinelli
Dr. Priya Deshpande
Dr. Cristinel Ababei
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

  • neural network
  • artificial intelligence
  • machine learning
  • deep learning
  • signal processing
  • sensor systems

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.

Related Special Issue

Published Papers (1 paper)

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

Research

19 pages, 1221 KB  
Article
Distributed Deep Learning in IoT Sensor Network for the Diagnosis of Plant Diseases
by Athanasios Papanikolaou, Athanasios Tziouvaras, George Floros, Apostolos Xenakis and Fabio Bonsignorio
Sensors 2025, 25(24), 7646; https://doi.org/10.3390/s25247646 - 17 Dec 2025
Viewed by 945
Abstract
The early detection of plant diseases is critical to improving agricultural productivity and ensuring food security. However, conventional centralized deep learning approaches are often unsuitable for large-scale agricultural deployments, as they rely on continuous data transmission to cloud servers and require high computational [...] Read more.
The early detection of plant diseases is critical to improving agricultural productivity and ensuring food security. However, conventional centralized deep learning approaches are often unsuitable for large-scale agricultural deployments, as they rely on continuous data transmission to cloud servers and require high computational resources that are impractical for Internet of Things (IoT)-based field environments. In this article, we present a distributed deep learning framework based on Federated Learning (FL) for the diagnosis of plant diseases in IoT sensor networks. The proposed architecture integrates multiple IoT nodes and an edge computing node that collaboratively train an EfficientNet B0 model using the Federated Averaging (FedAvg) algorithm without transferring local data. Two training pipelines are evaluated: a standard single-model pipeline and a hierarchical pipeline that combines a crop classifier with crop-specific disease models. Experimental results on a multicrop leaf image dataset under realistic augmentation scenarios demonstrate that the hierarchical FL approach improves per-crop classification accuracy and robustness to environmental variations, while the standard pipeline offers lower latency and energy consumption. Full article
Show Figures

Figure 1

Back to TopTop