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Machine Learning and Big Data Analytics for the Internet of Things and Wireless Sensor Networks

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 2512

Special Issue Editors

Dr. Emanuele Lattanzi

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, University of Urbino, 61029 Urbino, Italy
Interests: wireless sensor networks; machine learning; internet of things; embedded devices
Special Issues, Collections and Topics in MDPI journals
Dr. Chiara Contoli

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, University of Urbino, 61029 Urbino, Italy
Interests: internet of things; machine learning; softwarized and programmable networks; networked systems

Special Issue Information

Dear Colleagues,

The use of Machine Learning (ML) techniques has been highlighted as a promising approach for processing IoT data and optimizing resource utilization in wireless sensor networks (WSNs) and represents one of the most advanced analytical techniques currently in use.

The IoT and WSNs generate a large volume of data from various sources such as environmental sensors, smart appliances, industrial equipment, and wearable devices. ML algorithms help to process and analyze these data in order to extract meaningful insights, detect patterns, and make predictions.

The topics of interest within this Special Issue include, but are not limited to:

  • Predictive maintenance;
  • Anomaly detection;
  • Sensor fusion;
  • Energy optimization;
  • Tiny Machine Learning;
  • Data-driven model optimization;
  • Model pruning, quantization, and compression;
  • Interpretation and decision-making;
  • Smart cities and urban analytics;
  • Security and Privacy.

Dr. Emanuele Lattanzi
Dr. Chiara Contoli
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.

Keywords

  • Internet of Things
  • machine learning for IoT
  • energy-aware machine learning
  • predictive maintenance
  • big data analytics
  • wireless sensor networks
  • security and privacy

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

Published Papers (2 papers)

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Research

23 pages, 2239 KiB  
Article
Securing IoT Networks Against DDoS Attacks: A Hybrid Deep Learning Approach
by Noor Ul Ain, Muhammad Sardaraz, Muhammad Tahir, Mohamed W. Abo Elsoud and Abdullah Alourani
Sensors 2025, 25(5), 1346; https://doi.org/10.3390/s25051346 - 22 Feb 2025
Viewed by 1022
Abstract
The Internet of Things (IoT) has revolutionized many domains. Due to the growing interconnectivity of IoT networks, several security challenges persist that need to be addressed. This research presents the application of deep learning techniques for Distributed Denial-of-Service (DDoS) attack detection in IoT [...] Read more.
The Internet of Things (IoT) has revolutionized many domains. Due to the growing interconnectivity of IoT networks, several security challenges persist that need to be addressed. This research presents the application of deep learning techniques for Distributed Denial-of-Service (DDoS) attack detection in IoT networks. This study assesses the performance of various deep learning models, including Latent Autoencoders, LSTM Autoencoders, and convolutional neural networks (CNNs), for DDoS attack detection in IoT environments. Furthermore, a novel hybrid model is proposed, integrating CNNs for feature extraction, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Autoencoders for dimensionality reduction. Experimental results on the CICIOT2023 dataset show the enhanced performance of the proposed hybrid model, achieving training and testing accuracy of 96.78% integrated with 96.60% validation accuracy. This presents its efficiency in addressing complex attack patterns within IoT networks. Results’ analysis shows that the proposed hybrid model outperforms the others. However, this research has limitations in detecting rare attack types and emphasizes the importance of addressing data imbalance challenges for further enhancement of DDoS attack detection capabilities in future. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Analytics for the Internet of Things and Wireless Sensor Networks)
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14 pages, 730 KiB  
Article
Fully Scalable Fuzzy Neural Network for Data Processing
by Łukasz Apiecionek
Sensors 2024, 24(16), 5169; https://doi.org/10.3390/s24165169 - 10 Aug 2024
Cited by 1 | Viewed by 1225
Abstract
The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). [...] Read more.
The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Analytics for the Internet of Things and Wireless Sensor Networks)
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