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AI and Big Data in Internet of Things: Collection, Management and Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 2819

Special Issue Editors

Dr. Andreas P. Plageras

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: web design and development; application development; networks and security; systems programming; cloud and edge computing; big data technologies; big data analytics; artificial intelligence; automation; robotics; 3D graphics design
Special Issues, Collections and Topics in MDPI journals
Dr. Christos L. Stergiou

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: algorithms for cloud computing; big data; wireless communication
Special Issues, Collections and Topics in MDPI journals
Dr. Vasileios Memos

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: computer networks; wireless and mobile communications; multimedia transmission over 5G networks; cloud computing (CC); big data analytics (BDA); wireless sensor network (WSN); Internet of Things (IoT); artificial intelligence (AI); machine learning (ML); cyber security; cryptography; privacy and security software testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Konstantinos E. Psannis

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: digital signals and systems; 6G-enabled-ubiquitous big data; AI-IoT; clouds and communications; digital media communications; media coding; media synchronization; transport over a variety of networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world has changed. New technologies, trends, protocols, efficient algorithms, and systems have been widely adopted by developed countries to enable better and more convenient living. As applications based on the “Internet of Things” (IoT) have been widespread in every sector, many studies have provided useful information about new trends regarding the IoT, the issues that they cause, some solutions that already exist, and various challenges that need to be overcome in order to benefit from Internet technology.

Moreover, “Artificial Intelligence” (AI) is a technological advancement providing solutions to various issues. Such solutions are based on the efficient preprocessing and analysis of Big Data, although they can involve algorithms with high complexity.

This Special Issue aims to compile high-quality review papers on applied informatics. We encourage contributions from researchers in various disciplines within the journal’s scope that highlight the latest developments in AI, the IoT, and Big Data in their field. Alternatively, you may invite relevant experts and colleagues to do so.

The topics of interest for this Special Issue include, but are not limited to, the following:

  • Big Data delivery in the Internet of Artificially Intelligent Things;
  • Big Data management and analytics for various applications;
  • AI and Machine Learning applications;
  • AI in the IoT for sustainable Big Data management;
  • AI-driven UAV communications for time-critical IoT applications;
  • AI and ML for Autonomous Vehicles;
  • Efficient data delivery and analytics in various applications;
  • Intelligent Decision Systems for the IoT.

Dr. Andreas P. Plageras
Dr. Christos L. Stergiou
Dr. Vasileios Memos
Prof. Dr. Konstantinos E. Psannis
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. 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

  • AI-IoT
  • algorithms
  • analytics
  • applications
  • big data
  • machine learning

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Published Papers (4 papers)

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Research

22 pages, 2321 KB  
Article
A Deployment-Aware Data Processing Approach for Accuracy and Authenticity Evaluation of Artificial Emotional Intelligence in IoT Edge with Deep Learning
by Şükrü Mustafa Kaya
Appl. Sci. 2026, 16(9), 4394; https://doi.org/10.3390/app16094394 - 30 Apr 2026
Viewed by 479
Abstract
Artificial Emotional Intelligence (AEI) has gained significant attention for enabling machines to recognize and interpret human affective states through modalities such as speech. While deep learning-based speech emotion recognition (SER) models have achieved promising accuracy levels, their practical deployment in resource-constrained IoT edge [...] Read more.
Artificial Emotional Intelligence (AEI) has gained significant attention for enabling machines to recognize and interpret human affective states through modalities such as speech. While deep learning-based speech emotion recognition (SER) models have achieved promising accuracy levels, their practical deployment in resource-constrained IoT edge environments remains insufficiently explored. In particular, there is a lack of systematic evaluation approaches that jointly consider classification performance, computational efficiency, and deployment feasibility under edge-oriented operational constraints. In this study, I address this gap by proposing a deployment-aware evaluation perspective for SER systems operating under IoT edge constraints. Rather than introducing a new model architecture, I focus on establishing a unified and reproducible evaluation framework that reflects practical deployment considerations for edge-based intelligent systems. Within this framework, three widely used deep learning architectures, convolutional neural networks (CNN), long short-term memory (LSTM), and dense neural networks, are systematically analyzed using the EMODB dataset. The experimental results demonstrate that CNN-based models achieve the most consistent classification performance, with peak validation accuracy reaching approximately 84%, while also providing a favorable balance between recognition performance and computational efficiency. To better reflect deployment-oriented evaluation, the study also considers latency-related behavior and computational characteristics relevant to edge computing environments based on benchmark-driven estimations. The findings highlight the importance of deployment-aware evaluation strategies and provide practical insights for selecting suitable model architectures in edge-oriented speech emotion recognition scenarios. This study contributes to bridging the gap between theoretical deep learning performance and practical feasibility considerations in IoT-based intelligent systems. Full article
(This article belongs to the Special Issue AI and Big Data in Internet of Things: Collection, Management and Analysis)
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20 pages, 537 KB  
Article
Hybrid Blended WiFi Fingerprint Indoor Localization Using Multi-Task Learning and Feature-Space WKNN
by Yujie Li and Sang-Chul Kim
Appl. Sci. 2026, 16(9), 4184; https://doi.org/10.3390/app16094184 - 24 Apr 2026
Viewed by 231
Abstract
WiFi fingerprinting remains attractive for indoor localization because it reuses existing wireless infrastructure, yet RSSI fingerprints are high-dimensional, sparse, and often ambiguous across adjacent floors and building regions. This study develops a hybrid blended localization framework that combines multi-task learning with feature-space weighted [...] Read more.
WiFi fingerprinting remains attractive for indoor localization because it reuses existing wireless infrastructure, yet RSSI fingerprints are high-dimensional, sparse, and often ambiguous across adjacent floors and building regions. This study develops a hybrid blended localization framework that combines multi-task learning with feature-space weighted k-nearest-neighbor refinement. A shared neural encoder predicts building labels, floor labels, and normalized coordinates from 520-dimensional WiFi fingerprints, and the learned embedding space is then used for semantically constrained WKNN correction. The final model is trained with AdamW, a learning rate of 8×10−4, batch size 512, and a joint loss over building classification, floor classification, and coordinate regression, without a learning-rate scheduler. Experiments on a public WiFi fingerprint dataset show that the hybrid model achieves the strongest overall localization robustness among the evaluated non-ensemble methods. On the official validation split, it obtains a mean localization error of 9.01, a median error of 6.25, and an RMSE of 12.95 in the dataset coordinate units. On the internal semantic validation split, it reaches 94.81% floor classification accuracy and 97.62% building classification accuracy. Floor-wise and building–floor analyses further show that the largest errors are concentrated in a small number of difficult semantic regions, especially the highest floor and sparsely constrained partitions. Full article
(This article belongs to the Special Issue AI and Big Data in Internet of Things: Collection, Management and Analysis)
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22 pages, 2237 KB  
Article
TPP-TimeNet: A Time-Aware AI Framework for Robust Abnormality Detection in Bioprocess Monitoring
by Hye-Kyeong Ko
Appl. Sci. 2026, 16(7), 3295; https://doi.org/10.3390/app16073295 - 28 Mar 2026
Viewed by 442
Abstract
Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only [...] Read more.
Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article
(This article belongs to the Special Issue AI and Big Data in Internet of Things: Collection, Management and Analysis)
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16 pages, 1574 KB  
Article
On-Device Privacy-Preserving Fraud Detection for Smart Consumer Environments Using Federated Learning
by Alexandros I. Bermperis, Vasileios A. Memos, Christos L. Stergiou, Andreas P. Plageras and Konstantinos E. Psannis
Appl. Sci. 2026, 16(2), 835; https://doi.org/10.3390/app16020835 - 14 Jan 2026
Viewed by 1028
Abstract
This paper discusses an on-device artificial intelligence (AI) solution for real-time, privacy-preserving fraud detection in smart financial environments, ensuring privacy-preserving consumer transactions. We suggest a distributed, on-device fraud detection solution that uses federated learning (FL) to improve privacy while detecting fraudulent transactions efficiently [...] Read more.
This paper discusses an on-device artificial intelligence (AI) solution for real-time, privacy-preserving fraud detection in smart financial environments, ensuring privacy-preserving consumer transactions. We suggest a distributed, on-device fraud detection solution that uses federated learning (FL) to improve privacy while detecting fraudulent transactions efficiently across decentralized smart environments. In this work, we used several models, including reinforcement learning (RL) agent and Random Forest, and we tested their performance using several measures like accuracy, precision, recall, and F-score, ensuring their applicability to smart environments with resource constraints. The recommended mechanism also uses t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) to reduce dimensions of data, visualize the results, and evaluate the success rate of transactions classified as fraudulent and non-fraudulent. In our methodology, we applied data collection, data preprocessing, and cleaning, and we evaluated the metrics of selected models to allocate resources effectively and support decision-making processes in edge-based fraud detection systems within smart environments. Full article
(This article belongs to the Special Issue AI and Big Data in Internet of Things: Collection, Management and Analysis)
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