Topic Editors

Dr. Oleksandr Kuznetsov
Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy

Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration

Abstract submission deadline
30 September 2025
Manuscript submission deadline
31 December 2025
Viewed by
2434

Topic Information

Dear Colleagues,

The Internet of Things (IoT) continuously transforms how we interact with our world, enabling new and modern intelligent ecosystems through interconnected devices and systems. This Topic aims to explore modern and advanced groundbreaking architectures and applications that tackle the multifaceted challenges of modern IoT systems and devices, specifically focusing on advancing scalable, secure, and efficient systems. We invite contributions that want to push the boundaries of theoretical innovation and practical implementation, addressing critical needs in diverse domains.

The core areas of interest include innovative IoT architectural paradigms, the edge–fog–cloud computing continuum, resource-efficient protocols, and robust security and privacy frameworks. Applications integrating the artificial intelligence (AI) and machine learning (ML) landscape are particularly emphasized to enhance IoT systems' intelligence, adaptability, and autonomy. Topics such as sustainable and energy-efficient IoT designs, real-time performance optimization, and AI-driven automation are central to this collection.

We are especially keen to spotlight emerging applications that transform IoT and AI. These include leveraging IoT in smart cities, industrial automation, and healthcare; deploying AI-enabled IoT systems for environmental monitoring and disaster management; and pioneering space applications, such as satellite-based IoT networks, extraterrestrial resource monitoring, and interplanetary communication systems.

We highly encourage new research and studies that address the unique challenges of IoT in extreme environments, including space.

This Topic seeks to compile a comprehensive body of cutting-edge research that bridges theoretical advances and practical solutions, fostering collaboration between academia and industry. This collection is a unique occasion to shape the future of intelligent and interconnected systems across Earth and beyond by highlighting innovative IoT systems enhanced by AI and addressing the frontier of space applications.

Dr. Oleksandr Kuznetsov
Prof. Dr. Cristian Randieri
Topic Editors

Keywords

  • Internet of Things
  • edge computing
  • distributed systems architecture
  • IoT security
  • smart infrastructure
  • cyber–physical systems
  • AI-driven IoT
  • real-time systems
  • space-based IoT applications
  • network protocols
  • resource optimization

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
Future Internet
futureinternet
2.8 7.1 2009 16.9 Days CHF 1600 Submit
IoT
IoT
- 8.5 2020 27.8 Days CHF 1200 Submit
Technologies
technologies
4.2 6.7 2013 21.1 Days CHF 1600 Submit
Telecom
telecom
2.1 4.8 2020 20.5 Days CHF 1200 Submit
Big Data and Cognitive Computing
BDCC
3.7 7.1 2017 25.3 Days CHF 1800 Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

  1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
  2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
  3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
  4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
  5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
23 pages, 2456 KiB  
Article
FedRecI2C: A Novel Federated Recommendation Framework Integrating Communication and Computation to Accelerate Convergence Under Communication Constraints
by Qizhong Zheng and Xiujie Huang
Future Internet 2025, 17(3), 132; https://doi.org/10.3390/fi17030132 - 20 Mar 2025
Viewed by 98
Abstract
The federated recommender system (FRS) employs federated learning methodologies to create a recommendation model in a distributed environment, where clients share locally updated data with the server without exposing raw data and achieving privacy preservation. However, varying communication capabilities among devices restrict the [...] Read more.
The federated recommender system (FRS) employs federated learning methodologies to create a recommendation model in a distributed environment, where clients share locally updated data with the server without exposing raw data and achieving privacy preservation. However, varying communication capabilities among devices restrict the participation of only a subset of clients in each round of federated training, resulting in slower convergence and requiring additional training rounds. In this work, we propose a novel federated recommendation framework, called FedRecI2C, which integrates communication and computation resources in the system. This framework accelerates convergence by utilizing not only communication-capable clients for federated training but also communication-constrained clients to leverage their computation and limited communication resources for further local training. This framework offers simplicity and flexibility, providing a plug-and-play architecture that effectively enhances the convergence speed in FRSs. It has demonstrated remarkable effectiveness in a wide range of FRSs when operating under diverse communication conditions. Extensive experiments are conducted to validate the effectiveness of FedRecI2C. Moreover, we provide in-depth analyses of the FedRecI2C framework, offering novel insights into the training patterns of FRSs. Full article
Show Figures

Figure 1

19 pages, 13798 KiB  
Article
RANFIS-Based Sensor System with Low-Cost Multi-Sensors for Reliable Measurement of VOCs
by Keunyoung Kim and Woosung Yang
Technologies 2025, 13(3), 111; https://doi.org/10.3390/technologies13030111 - 7 Mar 2025
Viewed by 481
Abstract
This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that [...] Read more.
This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that were impossible to install, resulting in a lack of continuous management. This paper develops a low-cost sensor system for full-time management and consists of multi-sensor systems to increase the spatial resolution in the pipe. To improve the accuracy and reliability of this system, a new reinforced adaptive neuro fuzzy inference system (RANFIS) model with enhanced preprocessing based on the adaptive neuro fuzzy inference system (ANFIS) model is proposed. For this purpose, a smart sensor module consisting of low-cost metal oxide semiconductors (MOSs) and photo-ionization detectors (PIDs) is fabricated, and an operating controller is configured for real-time data acquisition, analysis, and evaluation. In the front part of the RANFIS, interquartile range (IQR) is used to remove outliers, and gradient analysis is used to detect and correct data with abnormal change rates to solve nonlinearities and outliers in sensor data. In the latter stage, the complex nonlinear relationship of the data was modeled using the ANFIS to reliably handle data uncertainty and noise. For practical verification, a toluene evaporation chamber with a sensor system for monitoring was built, and the results of real-time data sensing after training based on real data were compared and evaluated. As a result of applying the RANFIS model, the RMSE of the MQ135, MQ138, and PID-A15 sensors were 3.578, 11.594, and 4.837, respectively, which improved the performance by 87.1%, 25.9%, and 35.8% compared to the existing ANFIS. Therefore, the precision within 5% of the measurement results of the two experimentally verified sensors shows that the proposed RANFIS-based sensor system can be sufficiently applied in the field. Full article
Show Figures

Figure 1

43 pages, 112805 KiB  
Article
Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
by Tahesin Samira Delwar, Sayak Mukhopadhyay, Akshay Kumar, Mangal Singh, Yang-won Lee, Jee-Youl Ryu and A. S. M. Sanwar Hosen
Future Internet 2025, 17(2), 70; https://doi.org/10.3390/fi17020070 - 6 Feb 2025
Viewed by 1097
Abstract
This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of [...] Read more.
This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of Things (IoT), successfully safeguarding crops and reducing agricultural losses. This study involves a thorough examination of five models—Inception, Xception, VGG16, AlexNet, and YoloV8—against three different datasets. The YoloV8 model emerged as the most promising, with exceptional accuracy and precision, exceeding 99% in both categories. Following that, the YoloV8 model’s performance was compared to previous study findings, confirming its excellent capabilities in terms of intrusion detection in agricultural settings. Using the capabilities of the YoloV8 model, an IoT device was designed to provide real-time intrusion alarms on farms. The ESP32cam module was used to build this gadget, which smoothly integrated this cutting-edge model to enable efficient farm security measures. The incorporation of this technology has the potential to transform farm monitoring by providing farmers with timely, actionable knowledge to prevent possible threats and protect agricultural production. Full article
Show Figures

Figure 1

Back to TopTop