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Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations

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

Deadline for manuscript submissions: 20 April 2026 | Viewed by 19078

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Special Issue Editors

Dr. David Sarabia-Jácome

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Guest Editor

i2CAT Internet Research Center, 08034 Barcelona, Spain
Interests: IoT; artificial intelligence; edge computing; future networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Carlos Enrique Palau Salvador

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Guest Editor

Communications Department, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: IoT; EdgeAI; interoperability; edge computing; 5G/6G; SDN; networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is rapidly evolving into an advanced ecosystem that integrates cutting-edge intelligence, next-generation network architectures, and novel communication paradigms. These advancements enable smarter, more adaptive, and highly connected environments, driving transformative changes across multiple domains, including healthcare, smart cities, industrial automation, and intelligent transportation. As IoT ecosystems grow more sophisticated, the seamless convergence of artificial intelligence (AI), cloud/edge architectures, and novel networking approaches is unlocking new possibilities. Embedding AI into IoT devices enhances efficiency, enabling real-time, energy-conscious operations while supporting multi-agent collaboration and intelligent cooperation between distributed IoT nodes for improved adaptability and performance. Cloud/edge–IoT (CEI) continuum architecture is a key enabler in this evolution, providing a dynamic and scalable framework that bridges computational resources across cloud and edge environments, ensuring efficient data management and processing, reduced latency, and enhanced intelligence at multiple levels of the IoT stack. While, innovations in networking technologies—including 5G and 6G, ultra-reliable low-latency communication (URLLC), and AI-enhanced network optimization for IoT applications—are accelerating IoT adoption and enabling groundbreaking applications and services. The advances are fostering intelligent environments that leverage IoT, AI, and contextual awareness to enhance human–computer interactions and automation. Additionally, the integration of communication and sensing functionalities is optimizing IoT system performance, further propelling the next generation of smart, connected ecosystems.

This Special Issue aims to bring together academia and industrial researchers to propose new IoT architectures, intelligence integration, and novel communication strategies that will shape the future of IoT ecosystems. This Special Issue will publish high-quality, original research papers. The potential topics of interest include, but are not limited to, the following:

Cloud/edge–IoT continuum architectures;
IoT data management and advanced IoT analytics;
IoT applications and services;
Industry 4.0 and the industrial IoT (IIoT);
Advances in IoT protocols;
Next-generation infrastructure for the IoT;
IoT 5G/6G slice management;
IoT orchestration;
Future network architectures and protocols for the IoT;
Edge AI, embedded AI, and cooperative intelligence for the IoT;
Generative AI (GenAI) use cases for the IoT;
Dataspaces in IoT applications;
Green communication and the IoT;
Visible light communications for the IoT;
Ambient intelligence;
Case studies of implementation in IoT Ecosystems.

Dr. David Sarabia-Jácome
Prof. Dr. Carlos Enrique Palau Salvador
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

next-generation IoT
edge computing
GenIA
network
architecture
IoT applications
future networks
ambient intelligence

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Published Papers (8 papers)

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Research

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32 pages, 8611 KB

Open AccessArticle

Softwarized Edge Intelligence for Advanced IIoT Ecosystems: A Data-Driven Architecture Across the Cloud/Edge Continuum

by David Carrascal, Javier Díaz-Fuentes, Nicolas Manso, Diego Lopez-Pajares, Elisa Rojas, Marco Savi and Jose M. Arco

Appl. Sci. 2025, 15(19), 10829; https://doi.org/10.3390/app151910829 - 9 Oct 2025

Viewed by 865

Abstract

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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22 pages, 2738 KB

Open AccessArticle

Evaluation of a Lightweight IoT Protocol for Intelligent Parking Management in Urban Environments

by Fabrizio Messina, Miriana Russo, Corrado Santoro, Federico Fausto Santoro and Alessio Tudisco

Appl. Sci. 2025, 15(17), 9621; https://doi.org/10.3390/app15179621 - 1 Sep 2025

Viewed by 4433

Abstract

This work presents the design and evaluation of a distributed IoT protocol for intelligent parking management. It exploits the communication protocol LoRa and is designed to operate fully autonomously, without requiring Internet connectivity, enabling real-time parking slot detection and allocation through long-range wireless communication. The protocol also includes an optional MQTT-based synchronisation layer to support data exchange between gateways and with a central collector, allowing for telemetry, system monitoring, and analytics. We performed a set of experiments, proving that the protocol holds system resilience and scalability, which are key aspects for deployment in urban environments with unreliable or limited network access. We also observed a significant reduction in parking search time while preserving acceptable levels of system latency. To complete our evaluation, we deployed, in our laboratory, a test-bed made by ESP32-based nodes and simulated gateway breakage and replacement, in order to prove the fault recovery capabilities of the entire network. Finally, we conducted a few empirical stress tests simulating high communication traffic and interactions, confirming acceptable effectiveness and stability for real urban contexts. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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28 pages, 3464 KB

Open AccessArticle

Real-Time Intelligent Monitoring of Outdoor Air Quality in an Urban Environment Using IoT and Machine Learning Algorithms

by Osama Alsamrai, Maria D. Redel-Macias and M. P. Dorado

Appl. Sci. 2025, 15(16), 9088; https://doi.org/10.3390/app15169088 - 18 Aug 2025

Viewed by 4367

Abstract

The monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study addresses three challenges: (1) design of a low-cost compact, robust, multi-sensor system, (2) model validation over several months to ensure accurate detection, and (3) the application of machine learning (ML) techniques to classify and predict AQ. The developed system demonstrates a significant cost reduction for regular monitoring, including effective data management under harsh environmental conditions. The prototype integrates pollutant sensors, as well as the detection of liquified petroleum gas, humidity, and temperature. A dataset with more than 30,000 entries per month (data recorded approximately every minute) was saved on the platform. Results identified the three highest pollution categories, highlighting the urgency of addressing AQ in densely populated regions. The ML algorithms allowed us to predict AQ trends with 99.97% accuracy. To summarize, by reducing monitoring costs and enabling large-scale data management, this system offers an effective solution for real-time environmental monitoring. It also highlights the potential of artificial intelligence-based AQ predictions in supporting public health initiatives. This is particularly interesting for developing countries, where pollution control is limited. Future research will develop the models to include data from different environments and seasons, exploring its integration into mobile apps and cloud platforms for real-time monitoring. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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21 pages, 571 KB

Open AccessArticle

Joint Optimization of Caching and Recommendation with Performance Guarantee for Effective Content Delivery in IoT

by Zhiyong Liu, Hong Shen and Hui Tian

Appl. Sci. 2025, 15(14), 7986; https://doi.org/10.3390/app15147986 - 17 Jul 2025

Viewed by 847

Abstract

Content caching and recommendation for content delivery over the Internet are two key techniques for improving the content delivery effectiveness determined by delivery efficiency and user satisfaction, which is increasingly important in the booming Internet of Things (IoT). While content caching seeks the “greatest common denominator” among users to reduce end-to-end delay in content delivery, personalized recommendation, on the contrary, emphasizes users’ differentiation to enhance user satisfaction. Existing studies typically address them separately rather than jointly due to their contradictory objectives. They focus mainly on heuristics and deep reinforcement learning methods without the provision of performance guarantees, which are required in many real-world applications. In this paper, we study the problem of joint optimization of caching and recommendation in which recommendation is performed in the cached contents instead of purely according to users’ preferences, as in the existing work. We show the NP-hardness of this problem and present a greedy solution with a performance guarantee by first performing content caching according to user request probability without considering recommendations to maximize the aggregated request probability on cached contents and then recommendations from cached contents to incorporate user preferences for cache hit rate maximization. We prove that this problem has a monotonically increasing and submodular objective function and develop an efficient algorithm that achieves a

1 - \frac{1}{e} \approx 0.63

approximation ratio to the optimal solution. Experimental results demonstrate that our algorithm dramatically improves the popular least-recently used (LRU) algorithm. We also show experimental evaluations of hit rate variations by Jensen–Shannon Divergence on different parameter settings of cache capacity and user preference distortion limit, which can be used as a reference for appropriate parameter settings to balance user preferences and cache hit rate for Internet content delivery. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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31 pages, 9063 KB

Open AccessArticle

Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach

by Zohra Dakhia and Massimo Merenda

Appl. Sci. 2025, 15(13), 7556; https://doi.org/10.3390/app15137556 - 5 Jul 2025

Cited by 2 | Viewed by 3301

Abstract

Federated Learning (FL), a key paradigm in privacy-preserving and distributed machine learning (ML), enables collaborative model training across decentralized data sources without requiring raw data exchange. FL enables collaborative model training across decentralized data sources while preserving privacy. However, selecting appropriate clients remains a major challenge, especially in heterogeneous environments with diverse battery levels, privacy needs, and learning capacities. In this work, a centralized reward-based payoff strategy (RBPS) with cooperative intent is proposed for client selection. In RBPS, each client evaluates participation based on locally measured battery level, privacy requirement, and the model’s accuracy in the current round computing a payoff from these factors and electing to participate if the payoff exceeds a predefined threshold. Participating clients then receive the updated global model. By jointly optimizing model accuracy, privacy preservation, and battery-level constraints, RBPS realizes a multi-objective selection mechanism. Under realistic simulations of client heterogeneity, RBPS yields more robust and efficient training compared to existing methods, confirming its suitability for deployment in resource-constrained FL settings. Experimental analysis demonstrates that RBPS offers significant advantages over state-of-the-art (SOA) client selection methods, particularly those relying on a single selection criterion such as accuracy, battery, or privacy alone. These one-dimensional approaches often lead to trade-offs where improvements in one aspect come at the cost of another. In contrast, RBPS leverages client heterogeneity not as a limitation, but as a strategic asset to maintain and balance all critical characteristics simultaneously. Rather than optimizing performance for a single device type or constraint, RBPS benefits from the diversity of heterogeneous clients, enabling improved accuracy, energy preservation, and privacy protection all at once. This is achieved by dynamically adapting the selection strategy to the strengths of different client profiles. Unlike homogeneous environments, where only one capability tends to dominate, RBPS ensures that no key property is sacrificed. RBPS thus aligns more closely with real-world FL deployments, where mixed-device participation is common and balanced optimization is essential. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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19 pages, 1391 KB

Open AccessArticle

Edge-FLGuard: A Federated Learning Framework for Real-Time Anomaly Detection in 5G-Enabled IoT Ecosystems

by Manuel J. C. S. Reis

Appl. Sci. 2025, 15(12), 6452; https://doi.org/10.3390/app15126452 - 8 Jun 2025

Cited by 7 | Viewed by 2715

Abstract

The rapid convergence of 5G networks and Internet of Things (IoT) technologies has unlocked unprecedented connectivity and responsiveness across smart environments—but has also amplified cybersecurity risks due to device heterogeneity, data privacy concerns, and distributed attack surfaces. To address these challenges, we propose Edge-FLGuard, a federated learning and edge AI-based anomaly detection framework tailored for real-time protection in 5G-enabled IoT ecosystems. The framework integrates lightweight deep learning models—specifically autoencoders and LSTM networks—for on-device inference, combined with a privacy-preserving federated training pipeline to enable scalable, decentralized threat detection without raw data sharing. We evaluate Edge-FLGuard using both public (CICIDS2017, TON_IoT) and synthetic datasets under diverse attack scenarios including spoofing, DDoS, and unauthorized access. Experimental results demonstrate high detection accuracy (F1-score ≥ 0.91, AUC-ROC up to 0.96), low inference latency (<20 ms), and robustness against data heterogeneity and adversarial conditions. By aligning edge intelligence with secure, collaborative learning, Edge-FLGuard offers a practical and scalable cybersecurity solution for next-generation IoT deployments. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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Review

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37 pages, 581 KB

Open AccessReview

Federated Load Balancing in Smart Cities: A 6G, Cloud, and Agentic AI Perspective

by Rohin Gillgallon, Giacomo Bergami and Graham Morgan

Appl. Sci. 2025, 15(20), 10920; https://doi.org/10.3390/app152010920 - 11 Oct 2025

Cited by 1 | Viewed by 769

Abstract

Modern smart cities are comprised of multiple sensors, all with their own collection of communicating devices transmitting data towards cloud data centres for analysis. Smart cities have limited bandwidth resources, which, if not managed correctly, can lead to network bottlenecks. These bottlenecks are commonly addressed through bottleneck mitigation strategies and load balancing algorithms, which aim to maximise the throughput of a smart city’s network infrastructure. Network simulators are a crucial tool for developing and testing bottleneck mitigation and load balancing techniques before deployment in real systems; however, many network simulators are developed as single-purpose tools, aiming to simulate a particular subset of an overarching use case. Such tools are therefore unable to model a real-world smart city infrastructure, which receives communications across a wide range of scenarios and from a wide variety of devices. This paper surveys the current state-of-the-art for network simulation tools, modern bottleneck mitigation strategies and load balancing techniques, evaluating each in terms of its suitability for smart cities and smart city simulation. This survey finds there is a lack of current network simulation tools up to the task of modelling smart city infrastructure and found no such simulation tools capable of modelling both smart city infrastructure and implementing the state-of-the-art bottleneck mitigation and load balancing strategies outlined within this work, highlighting this as a significant gap in current research before providing future work suggestions, including a federated approach for future simulation tools. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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26 pages, 1089 KB

Open AccessReview

Collecting, Integrating and Processing IoT Sensor Data on Edge Devices for PD Monitoring: A Scoping Review

by Eleftherios Efkleidis Stefanou, Pavlos Bitilis, Georgios Bouchouras and Konstantinos Kotis

Appl. Sci. 2025, 15(19), 10541; https://doi.org/10.3390/app151910541 - 29 Sep 2025

Viewed by 1046

Abstract

Bradykinesia and tremor are critical motor symptoms in diagnosing and monitoring Parkinson’s disease (PD), a progressive neurodegenerative disorder. The integration of IoT sensors, smartwatch technology, and edge computing has facilitated real-time collection, processing, and analysis of data related to these impairments, enabling continuous monitoring of PD beyond traditional clinical settings. This survey provides a comprehensive review of recent technological advancements in data collection from wearable IoT sensors and its semantic integration and processing on edge devices, emphasizing methods optimized for efficient and low-latency processing. Additionally, this survey explores AI-driven techniques for detecting and analyzing bradykinesia and tremor symptoms on edge devices. By leveraging localized computation on edge devices, these approaches facilitate energy efficiency, data privacy, and scalability, making them suitable for deployment in real environments. This paper also examines related open-source tools and datasets, assessing their roles in improving reproducibility and integration into these environments. Furthermore, key challenges, including variability in real environments, model generalization, and computational constraints, are discussed, along with potential strategies to enhance detection accuracy and system robustness. By bridging the gap between sensor data collection and integration, and AI-based detection of bradykinesia and tremor on edge devices, this survey intends to contribute to the development of efficient, scalable, and privacy-preserving healthcare solutions for continuous PD monitoring. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

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