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

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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

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

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Published Papers (5 papers)

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All AI Drones Electronics Future Internet IoT Technologies Telecom BDCC

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21 pages, 2693 KiB  

Open AccessArticle

Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices

by Anargul Shaushenova, Oleksandr Kuznetsov, Ardak Nurpeisova and Maral Ongarbayeva

Technologies 2025, 13(4), 155; https://doi.org/10.3390/technologies13040155 - 12 Apr 2025

Viewed by 269

Abstract

This research investigates the implementation of Kolmogorov–Arnold networks (KANs) for image processing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant advantages over traditional deep learning approaches, particularly in applications where computational resources are limited. Our study demonstrates [...] Read more.

This research investigates the implementation of Kolmogorov–Arnold networks (KANs) for image processing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant advantages over traditional deep learning approaches, particularly in applications where computational resources are limited. Our study demonstrates the efficiency of KAN-based solutions for image analysis tasks in IoTs environments, providing comparative performance metrics against conventional convolutional neural networks. The experimental results indicate substantial improvements in processing speed and memory utilization while maintaining competitive accuracy. This work contributes to the advancement of AI-driven IoTs applications by proposing optimized KAN-based implementations suitable for edge computing scenarios. The findings have important implications for IoTs deployment in smart infrastructure, environmental monitoring, and industrial automation where efficient image processing is critical. Full article

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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23 pages, 3430 KiB  

Open AccessArticle

Joint Optimization of Task Completion Time and Energy Consumption in UAV-Enabled Mobile Edge Computing

by Hanwen Zhang, Tao Chen, Bangbang Ren, Ruozhe Li and Hao Yuan

Drones 2025, 9(4), 274; https://doi.org/10.3390/drones9040274 - 3 Apr 2025

Viewed by 220

Abstract

Unmanned Aerial Vehicles (UAVs) hold great promise for Mobile Edge Computing (MEC) owing to their flexible mobility, rapid deployment, and low-cost characteristics. However, UAV-enabled MEC still faces challenges in terms of the real-time arrival of computational tasks, energy reservation, and the actual response [...] Read more.

Unmanned Aerial Vehicles (UAVs) hold great promise for Mobile Edge Computing (MEC) owing to their flexible mobility, rapid deployment, and low-cost characteristics. However, UAV-enabled MEC still faces challenges in terms of the real-time arrival of computational tasks, energy reservation, and the actual response efficiency of the system. In this study, we focus on a UAV-enabled MEC scenario, where multiple UAVs function as airborne edge servers, offering computation services to multiple ground-based user devices (UDs). We aim to minimize the cost of the MEC system by optimizing the computation offloading policy. Specifically, we take task latency into account to ensure the timeliness of real-time tasks. The Lyapunov optimization method is employed to maintain a uniform and stable queue for energy consumption. Additionally, we draw on the concept of maximum completion time in shop-floor scheduling to optimize the actual response latency. To this end, we propose a joint optimization algorithm. First, the joint optimization problem is transformed into a per-time-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a reinforcement learning method, LyraRD, is proposed to solve the PROP. Experimental results verify that the proposed approach outperforms the benchmarks in terms of system performance. Full article

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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22 pages, 2456 KiB  

Open AccessArticle

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 200

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

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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19 pages, 13798 KiB  

Open AccessArticle

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 786

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

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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43 pages, 112805 KiB  

Open AccessArticle

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 1948

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

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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