Green Industrial Internet of Things (GIIoT) for Sustainable Smart Cities

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 1657

Special Issue Editors


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Guest Editor
Escola Superior de Cieencias Empresariais, Instituto Politécnico de Viana do Castelo (Polytechnic University of Viana do Castelo), 4900 Viana do Castelo, Portugal
Interests: MaaS; flexible transporte; smart cities; cybersecurity

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Guest Editor
Instituto Superior De Engenharia do Porto, 4249 Porto, Portugal
Interests: MaaS; flexible transporte; smart cities; project management

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Guest Editor
Escola Superior de Ciências Empresariais, Instituto Politécnico de Viana do Castelo (Polytechnic University of Viana do Castelo), 4900 Viana do Castelo, Portugal
Interests: MaaS; flexible transporte; smart cities

Special Issue Information

Dear Colleagues,

The development of the Internet of Things (IoT) technology and its integration in smart cities have changed the way we work and live, and have enriched our society. However, IoT technologies present several challenges such as increases in energy consumption and toxic pollution production, as well as E-waste in smart cities. Smart city applications must be environmentally friendly and, hence, require a move towards the green IoT. The green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, sustaining the environment, and cost management. To successfully accomplish this vision, cognitive IoT solutions are needed to reshape the existing smart applications toward further sustainable services in a more sustainable smart city. This Special Issue brings together a broad multidisciplinary community studying cognitive architectures across science and engineering. It aims to integrate ideas, theories, models, architectures, and techniques from across different disciplines that can enable the green Industrial Internet of Things (GIIoT) to thrive for sustainable smart cities. Potential topics include, but are not limited to, the following:

  • smart green logistics and transportation;
  • mobility as a service bases platforms, services and architectures;
  • IoT communication protocols for sustainable smart cities;
  • cognitive resource management in GIIoT;
  • ML and intelligent GIIoT-based localization;
  • intelligent blockchain for sustainable cities;
  • AI algorithms for GIIoT applied on smart cities;
  • enablers for intelligent/secured GIIoT;
  • use cases enabled by intelligent GIIoT in smart cities;
  • intelligent 5G/6G communication for sustainable cities.

Prof. Dr. Luís Barreto
Dr. António Amaral
Dr. Sara Baltazar
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. Smart Cities 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 2000 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 cities
  • GIIoT
  • artificial intelligence

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Published Papers (1 paper)

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Research

21 pages, 2584 KiB  
Article
Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings
by David Cabezuelo, Izar Lopez-Ramirez, June Urkizu and Ander Goikoetxea
Smart Cities 2025, 8(1), 3; https://doi.org/10.3390/smartcities8010003 - 24 Dec 2024
Viewed by 1041
Abstract
Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one [...] Read more.
Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations. Full article
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