Digital Twin and IoT

Dear Colleagues,

Digital twin (DT) is a powerful concept for the Internet of Things (IoT), offering substantial benefits across various industries such as manufacturing, healthcare, transportation, and more. By creating virtual replicas or representations of physical objects, processes, or systems, DT can significantly enhance both the development and operational phases of IoT networks. DT can facilitate real-time monitoring of physical entities or systems by continuously collecting data from embedded sensors, thus providing immediate insights into their behaviour, performance, and condition. This capability can enable proactive maintenance, predictive analytics, and operational optimization. Moreover, DTs can allow for simulation and what-if analysis of physical entities and systems, identifying inefficiencies, optimizing processes, and improving resource utilization while predicting equipment failures or maintenance needs before they occur.

Despite many benefits that can result from incorporating DT technology into IoT (DT-IoT), there are still issues and challenges to be addressed, including data security and privacy, data quality and integrity, interoperability, scalability, energy efficiency, cost, and ethical concerns. In this Special Issue, we aim to bring together high-quality contributions that address the aforementioned challenges. Topics covered will include, but are not limited to, the following:

• Architectures and standards for DT-IoT;
• Security and privacy solutions for DT-IoT;
• Machine learning and AI for DT-IoT;
• Communications issues in DT-IoT;
• Societal and ethical aspects of DT-IoT;
• Resource allocation and optimization;
• Implementation challenges;
• Service caching and DT mobility management.

Dr. Samuel D. Okegbile
Dr. Fabrizio Marozzo
Dr. Dan García Carrillo
Guest Editors

Manuscript Submission Information

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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.

• digital twin
• Internet of Things
• data security
• privacy
• interoperability
• scalability
• machine learning
• artificial intelligence
• ethical concerns
• optimization