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Future Internet
Special Issues
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). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1290

Special Issue 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-blind 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 (1 paper)

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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
Viewed by 823
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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