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IoT and Big Data Analytics for Smart Cities

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 25 August 2024 | Viewed by 711

Special Issue Editors


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Guest Editor
Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: database systems; big data; machine learning; decision support systems; energy management systems; IoT; digital twins
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: IoT; database systems; big data; deep learning; natural language processing; energy management systems; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities involve real-time communication between sensors and the Internet of Things (IoT), as well as the integration, processing, and analysis of large volumes of data collected from heterogenous sources that are referred to big data. Recent advances in IoT and big data contribute to the design of new models and development of innovative solutions for smart cities that can bring benefits to communities, environment, urban authorities, and industry. Even so, there are still many research gaps to be identified and solved to help urban communities in their transition towards a more green and sustainable development. This Special Issue aims to provide opportunities for researchers and practitioners to publish innovative and original papers that explore the challenges related to IoT and big data processing, integration, and analytics for smart cities. Both theoretical and practical approaches are welcome. Potential topics include, but are not limited to, the following: communication-based frameworks for IoT networks; data-driven architectures and models for real time data processing; IoT data integration; edge-fog-cloud computing for smart cities; automation and optimization models for smart cities; AI-based data processing; machine learning (ML) and deep learning (DL) for IoT networks and big data related to smart cities; natural language processing (NLP) for big data analytics; digital threads and digital twins for smart cities; and advanced analytics for smart cities.

Prof. Dr. Adela Bara
Dr. Simona-Vasilica Oprea
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. Sensors 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 2600 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
  • IoT networks
  • big data processing
  • big data analytics
  • edge-fog-cloud computing
  • AI-based data processing
  • digital twins

Published Papers (1 paper)

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Research

21 pages, 927 KiB  
Article
A Recommendation System for Prosumers Based on Large Language Models
by Simona-Vasilica Oprea and Adela Bâra
Sensors 2024, 24(11), 3530; https://doi.org/10.3390/s24113530 - 30 May 2024
Abstract
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min [...] Read more.
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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