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Future Internet
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Future and Smart Internet of Things
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Future and Smart Internet of Things

A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Internet of Things".

Deadline for manuscript submissions: 31 May 2027 | Viewed by 7654

Editors

Dr. Gayathri Karthick

E-Mail Website
Guest Editor
Department of Data Science and Computer Science, York St John University, London E14 2BA, UK
Interests: secure cloud environments; data analytics; machine learning; intelligent media edge computing
Dr. Sahar Ahmadzadeh

E-Mail Website
Guest Editor
Department of Data Science and Computer Science, York St John University, London E14 2BA, UK
Interests: Internet of Things; artificial intelligence; wireless sensor networks; data analysis; machine learning
Dr. Shamsuddeen Hassan Muhammad

E-Mail Website
Guest Editor
Department of Computing, Imperial College London, Huxley Building, 180 Queen's Gate, South Kensington, London SW7 2RH, UK
Interests: machine learning; deep learning; natural language processing; artificial intelligence
Dr. Aminu Bello Usman

E-Mail Website
Guest Editor
Department of Data Science and Computer Science, York St John University, London E14 2BA, UK
Interests: Internet of Things; secure packet-oriented network; data privacy; applied AI

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) continues to evolve, reshaping the way we interact with physical environments and enabling intelligent automation across diverse domains. As we progress toward a more connected future, the development of smart, secure, and adaptive IoT solutions is becoming increasingly vital. The next generation of IoT, termed the Future and Smart Internet of Things (FSIoT), promises enhanced intelligence, interoperability, and sustainability, driving innovation in fields such as smart homes, smart healthcare, industry 5.0, environmental monitoring, transportation, and beyond.

This Special Issue on "Future and Smart Internet of Things" aims to explore cutting-edge research and practical developments that push the boundaries of IoT technologies. It particularly encourages original contributions focusing on the convergence of IoT with Artificial Intelligence (AI), Machine Learning (ML), Blockchain, edge cloud computing, fog cloud computing, cybersecurity, and digital twins, which, collectively, form the backbone of resilient and scalable smart systems.

We invite the submission of articles that delve into innovative architectures, frameworks, algorithms, and real-world applications, with a focus on sustainability, security, and human-centric design. This Special Issue also welcomes interdisciplinary research that addresses the socio-economic and environmental impacts of future IoT systems.

Topics of interest include, but are not limited to, the following:

  • Intelligent and adaptive IoT systems for smart environments;
  • AI and ML-driven data analytics in future IoT networks;
  • Secure and scalable architectures for next-generation IoT;
  • Blockchain and decentralized technologies for IoT trust management;
  • Integration of digital twins in IoT ecosystems;
  • Edge, fog, and cloud computing models in IoT deployments;
  • IoT-based solutions for sustainability of energy, agriculture, and the climate;
  • Smart sensing and communication protocols for future IoT;
  • Privacy-preserving data sharing and storage in IoT systems;
  • Human-centric and context-aware IoT applications;
  • IoT innovations in healthcare, education, industry, and smart governance;
  • Case studies, experimental results, and real-world deployments;
  • Challenges and opportunities in designing future IoT systems;
  • Policy and ethical considerations in smart IoT adoption.

This Special Issue will offer a platform for sharing high-quality research that shapes the vision of smart and sustainable IoT ecosystems. Both academic researchers and industry practitioners are encouraged to submit original manuscripts that contribute toward building a smarter and more interconnected world.

Dr. Gayathri Karthick
Dr. Sahar Ahmadzadeh
Dr. Shamsuddeen Hassan Muhammad
Dr. Aminu Bello Usman
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly 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 1800 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

  • smart IoT systems
  • machine learning for IoT
  • AI-enabled IoT
  • blockchain in IoT
  • IoT security and privacy
  • edge and fog computing
  • sustainable IoT
  • digital twins
  • IoT data analytics
  • IoT in smart cities
  • intelligent sensing
  • human-centric IoT
  • cyber threat IoT
  • context-aware IoT
  • next-generation networks
  • decentralized IoT architectures
  • Industry 5.0
  • green IoT
  • IoT-based decision making

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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25 pages, 14317 KB  
Article
Security-by-Design and Risk-Based Certification for AI-Enabled Smart Home
by Iván Ortiz-Garcés and Roberto Andrade
Future Internet 2026, 18(9), 453; https://doi.org/10.3390/fi18090453 - 26 Aug 2026
Viewed by 225
Abstract
The integration of Artificial Intelligence (AI) into Internet of Things (IoT) ecosystems has enabled the development of advanced cyber–physical systems, including smart appliances, while introducing security, privacy, and AI governance risks that extend beyond the scope of traditional threat models. Existing approaches often [...] Read more.
The integration of Artificial Intelligence (AI) into Internet of Things (IoT) ecosystems has enabled the development of advanced cyber–physical systems, including smart appliances, while introducing security, privacy, and AI governance risks that extend beyond the scope of traditional threat models. Existing approaches often address cybersecurity, AI risk management, and regulatory compliance in isolation, leaving manufacturers without a systematic method for translating identified threats into architectural controls and certification requirements. To address this gap, this study proposes a Security-by-Design and risk-based certification framework that combines a six-layer IoT-AI reference architecture with STRIDE-based threat analysis augmented to capture AI-specific threats, including prompt injection and data poisoning. The resulting cross-layer analysis informs a four-level certification model (L1–L4) that deterministically maps each appliance configuration to a set of mandatory security and governance controls according to its degree of autonomy and AI capability. The framework is instantiated and evaluated using a physical smart-refrigerator prototype, demonstrating how threat identification can be systematically translated into design decisions and certification requirements. The proposed framework provides manufacturers, certification bodies, and researchers with a reproducible engineering pathway for designing and evaluating secure, governance-aligned AI-enabled IoT appliances. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
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28 pages, 947 KB  
Article
An AI Answer-Validation Method Using Agentic RAG for Datasheet Inquiry for IoT Application System Deployment
by Dezheng Kong, Nobuo Funabiki, Htoo Htoo Sandi Kyaw, I Nyoman Darma Kotama and Zihao Zhu
Future Internet 2026, 18(8), 442; https://doi.org/10.3390/fi18080442 - 19 Aug 2026
Viewed by 392
Abstract
Internet of Things (IoT) application systems are increasingly adopted in factories, shops, offices, and governments. However, building such systems using various devices and modules remains difficult for non-experts, because they must confirm specifications, communication interfaces, voltage ranges, and operating conditions from technical datasheets [...] Read more.
Internet of Things (IoT) application systems are increasingly adopted in factories, shops, offices, and governments. However, building such systems using various devices and modules remains difficult for non-experts, because they must confirm specifications, communication interfaces, voltage ranges, and operating conditions from technical datasheets before connecting devices. In previous studies, we have explored a generative AI-based answering tool for datasheet inquiry using Retrieval-Augmented Generation (RAG) for technical guidance of IoT application system deployment. However, the adopted top-kRAG pipeline often retrieves multiple related text chunks, which can cause the AI to confuse technically different specifications, such as power output voltage, signal output voltage, and input voltage range, and produce inaccurate answers. In addition, the AI may generate a hallucinated answer if the datasheet does not provide sufficient source information. In this paper, we propose an AI answer-validation method using agentic RAG for datasheet inquiry for IoT application system deployment. The method organizes datasheet information into structured specification data, including device models, field types, values, units, conditions, and source information. For question-answering, the agent coordinates structured fact query, top-k text retrieval, source checking, and rule-based compatibility comparison according to the question type. Instead of fully relying on the LLM to interpret retrieved chunks, this method adopts structured specifications and deterministic source checks before accepting the final answer. For evaluation, we constructed a dataset from 20 IoT datasheets, including 1000 question-answering tasks with three difficulty levels. Compared with conventional top-k RAG, the proposed method improved the correct answer rate from 0.686 to 0.958 for easy questions, from 0.549 to 0.969 for medium questions, and from 0.273 to 0.613 for hard questions, which confirms the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
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32 pages, 6300 KB  
Article
Multi-Protocol IoT Gateway Architecture: A Unified Approach to Smart-Home Connectivity
by Vasilios A. Orfanos, Stavros D. Kaminaris, Panagiotis Papageorgas, Dimitrios Piromalis and Dionisis Kandris
Future Internet 2026, 18(5), 255; https://doi.org/10.3390/fi18050255 - 11 May 2026
Viewed by 1664
Abstract
The Internet of Things (IoT) has a decentralized smart home ecosystem, as each protocol has its own gateway infrastructure needs. This study advances gateway convergence by proposing and rigorously evaluating a scalable architectural framework for future smart-home infrastructure. Specifically, this paper provides a [...] Read more.
The Internet of Things (IoT) has a decentralized smart home ecosystem, as each protocol has its own gateway infrastructure needs. This study advances gateway convergence by proposing and rigorously evaluating a scalable architectural framework for future smart-home infrastructure. Specifically, this paper provides a detailed analysis of a proposed integrated multi-protocol gateway design that supports 18 of the most widely used IoT communication protocols simultaneously. It is a one-device implementation combining wireless technologies, including short-range radios (Sub-1 GHz, 2.4 GHz), LPWANs (Long Power Wide Area Networks), cellular (LTE, Long-Term Evolution), and wired (Ethernet, KNX). Using the ns-3 network simulator, this paper shows that this architecture is practical in a simulated smart-home environment with a large number of interconnected devices distributed across various zones. The results demonstrate substantial reductions in energy consumption and operational complexity, without compromising quality of service across heterogeneous communication technologies. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
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25 pages, 3620 KB  
Article
Machine Learning for Assessing Vital Signs in Humans in Smart Cities Based on a Multi-Agent System
by Nejood Faisal Abdulsattar, Hassan Khotanlou and Hatam Abdoli
Future Internet 2026, 18(1), 27; https://doi.org/10.3390/fi18010027 - 2 Jan 2026
Cited by 2 | Viewed by 1700
Abstract
Healthcare professionals face numerous challenges when analyzing data and providing treatment, including determining which parameters to measure, the frequency of measurement, i.e., how frequently to measure them, and the responsibility for monitoring patient health with new medical devices. Machine learning (ML) techniques are [...] Read more.
Healthcare professionals face numerous challenges when analyzing data and providing treatment, including determining which parameters to measure, the frequency of measurement, i.e., how frequently to measure them, and the responsibility for monitoring patient health with new medical devices. Machine learning (ML) techniques are efficient predictive models used to improve early prediction of patient care and reduce the cost of implementing healthcare systems. This study proposes a new model (data prediction and labeling using a negative feature based on a multi-agent system (PLPF-MAS)) that provides a smart city-based healthcare system for the continuous monitoring of patients’ vital signs, such as heart rate, blood pressure, respiratory rate, and blood oxygen saturation. It also predicts future states and provides suitable recommendations based on clinical events. The MIMIC-II database of the MIT physio bank archive is used, which contains 1023 patient records. Additionally, the EHR dataset is used, which contains 10,000 patient records. The models were trained and evaluated for six bio-signals. The PLPF-MAS model is distinguished from traditional methods in its advanced system, which combines the activities of several agents and the intelligent distribution of responsibilities among them. The LR agent measures the model’s reliability in parallel with the AE-HMM agent to predict the Prisk; it then sends the data to a coordinator and a supervisory agent to monitor and manage the model. Our model is characterized by strong flexibility and reliability, the ability to deal with large datasets, and a short response time. It provides recommendations and warnings about risks, and it can predict clinical states with high accuracy. The new model achieved an accuracy of 98.4%, a precision of 95.3%, a sensitivity of 99.2%, a specificity of 99.1%, an F1-Score of 97.1%, and an R2 of 98%, when the MIMIC-II dataset was used. Conversely, it achieved an accuracy of 93%, a precision of 92%, a recall of 94%, an F1-Score of 93%, an AUC-ROC of 94%, and an AUC-PR of 89% when the EHR dataset was used. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
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Review

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23 pages, 953 KB  
Review
Large Language Models for Future Internet Ecosystems: A Taxonomy-Based Review of Smart IoT, Edge Intelligence, and Autonomous AI
by Sahar Ahmadzadeh, Gayathri Karthick and Tariq Alsafi
Future Internet 2026, 18(8), 393; https://doi.org/10.3390/fi18080393 - 26 Jul 2026
Viewed by 828
Abstract
Large Language Models (LLMs) are emerging as key enablers of Future Internet ecosystems, supporting intelligent, adaptive, and context-aware services across distributed cyber-physical environments. Beyond traditional natural language processing, LLMs are increasingly integrated into Smart Internet of Things (SIoT) systems to enable semantic interoperability, [...] Read more.
Large Language Models (LLMs) are emerging as key enablers of Future Internet ecosystems, supporting intelligent, adaptive, and context-aware services across distributed cyber-physical environments. Beyond traditional natural language processing, LLMs are increasingly integrated into Smart Internet of Things (SIoT) systems to enable semantic interoperability, edge intelligence, multimodal interaction, and autonomous service orchestration. This paper presents a taxonomy-based review of LLM architectures, training paradigms, deployment strategies, and emerging applications within Future Internet infrastructures. The review classifies existing studies by deployment environment, architecture, training strategy, accessibility, and application scope, and analyses the role of LLMs in intelligent IoT environments, with emphasis on edge-based reasoning, agentic AI, human-centric automation, and context-aware decision-making. Key challenges are examined, including scalability, inference latency, privacy, trustworthiness, security, hallucination, and energy efficiency in resource-constrained environments. A comparative analysis of representative LLMs is presented, based on deployment feasibility, multimodal capability, accessibility, and suitability for distributed intelligent services. The originality of the review lies in conceptualizing LLMs as cognitive middleware that provides semantic, reasoning, and coordination capabilities across Smart IoT infrastructures. Finally, future research directions are highlighted, including decentralized AI architectures, digital twins, the Model Context Protocol, retrieval-augmented generation, multimodal sensing, and autonomous agent-based ecosystems. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
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31 pages, 11663 KB  
Review
IoT Security: A Comprehensive Review of Architectures, Threat Models, Detection Methods, and Countermeasures
by Mehdi Moucharraf, Mohammed Ridouani, Fatima Salahdine and Naima Kaabouch
Future Internet 2026, 18(5), 266; https://doi.org/10.3390/fi18050266 - 18 May 2026
Cited by 1 | Viewed by 1929
Abstract
By allowing continuous connectivity, automation, and data-driven decision-making across these areas, Internet of Things (IoT) has transformed certain facets of daily life, including home automation and healthcare, as well as business operations like supply chain management and smart manufacturing. IoT systems are susceptible [...] Read more.
By allowing continuous connectivity, automation, and data-driven decision-making across these areas, Internet of Things (IoT) has transformed certain facets of daily life, including home automation and healthcare, as well as business operations like supply chain management and smart manufacturing. IoT systems are susceptible to different cyberattacks, though, because of different designs, lack of funds, and inadequate security policies, which creates major security issues given their fast growth. Covering important topics including protocols, architectures, attack classification, detection methods, countermeasures, and research issues, this paper offers a thorough study of IoT security. Emphasizing their relevance in enhancing the security of IoTs, the article offers a thorough analysis of machine and deep learning-based detection techniques. It also offers recommendations for future paths to handle changing risks by means of particular proposals and provides tools and datasets required for IoT security studies. When considering recent progress, however, there are still some major limitations in scaling, real-time detection, dataset availability, and versatility of current solutions. We identified these issues and provided guidance on future research; we also offered a selected set of tools and datasets for further research. Additionally, this paper provides an overview of the most important issues related to IoT security as documented in the current literature, providing a framework for developing resilient and adaptable IoT security solutions in the future. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
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