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

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 October 2025 | Viewed by 1762

Special Issue Editors

Dr. David Sarabia-Jácome

E-Mail Website
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

E-Mail Website
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 (3 papers)

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Research

21 pages, 571 KiB  
Article
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 141
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 [...] Read more.
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 1−1e≈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 KiB  
Article
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
Viewed by 347
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 [...] Read more.
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 KiB  
Article
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
Viewed by 923
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 [...] Read more.
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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