Machine Learning in Internet of Things II

Topic Information

Dear Colleagues,

Significant technological developments in the fields of machine learning and technology contribute to the expansion of the Internet of Things (IoT). The capabilities of knowledge extraction, data analysis, classification and inference are used in many systems. Research and implementation development allow us to expand the boundaries of machine learning applications. In the context of IoT, this will enable increasing the possibilities of automation, reasoning and decision-making based on the acquired data.

In recent years, there have also been other directions of development, such as digital twins and communication, which are very important. We dedicate this second edition to subsequent publications focused on intelligent solutions in the Internet of Things. Scientific research can focus on various applications in IoT, as well as on machine learning methods that are applicable to a broad range of smart materials.

Dr. Dawid Połap
Prof. Dr. Robertas Damaševičius
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
IoT IoT	-	8.5	2020	27.8 Days	CHF 1200	Submit
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	20.8 Days	CHF 1800	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

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All AI Drones Electronics IoT MAKE Sensors

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22 pages, 1174 KiB  

Open AccessArticle

Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks

by Joseph Azar, Mohammed Al Saleh, Raphaël Couturier and Hassan Noura

IoT 2025, 6(2), 22; https://doi.org/10.3390/iot6020022 - 26 Mar 2025

Viewed by 373

Abstract

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network [...] Read more.

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate. Full article

(This article belongs to the Topic Machine Learning in Internet of Things II)

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21 pages, 12287 KiB  

Open AccessFeature PaperArticle

An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare

by Arfan Ghani, Akinyemi Aina and Chan Hwang See

IoT 2024, 5(4), 901-921; https://doi.org/10.3390/iot5040041 - 6 Dec 2024

Cited by 2 | Viewed by 1298

Abstract

In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized implementation of a Convolutional Neural Network (CNN) on the Basys3 [...] Read more.

In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized implementation of a Convolutional Neural Network (CNN) on the Basys3 FPGA, designed specifically for accelerating the classification of cytotoxicity in human kidney cells. Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. The CNN, quantized to 8-bit precision and tailored for the FPGA’s resource constraints, significantly accelerates training by a factor of three while consuming only 1.33% of the power compared to a traditional software-based CNN running on an NVIDIA K80 GPU. The network architecture, composed of seven layers with excessive hyperparameters, processes downscale grayscale images, achieving notable gains in speed and energy efficiency. A cornerstone of our methodology is the emphasis on parallel processing, data type optimization, and reduced logic space usage through 8-bit integer operations. We conducted extensive image pre-processing, including histogram equalization and artefact removal, to maximize feature extraction from the augmented dataset. Achieving an accuracy of approximately 91% on unseen images, this FPGA-implemented CNN demonstrates the potential for rapid, low-power medical diagnostics within a broader IoT ecosystem where data could be assessed online. This work underscores the feasibility of deploying resource-efficient AI models in environments where traditional high-performance computing resources are unavailable, typically in healthcare settings, paving the way for and contributing to advanced computer vision techniques in embedded systems. Full article

(This article belongs to the Topic Machine Learning in Internet of Things II)

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