Smart Data and Systems for the Internet of Things

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 6940

Special Issue Editor

Special Issue Information

Dear Colleagues,

Every day we are faced with devices that use autonomous and smart technologies to perform their actions. The need to have intelligent systems using data from the Internet of Things (IoT) has become essential in a society that is becoming even more digital. This need was enforced with the appearance of emerging topics like smart cities, pervasive healthcare or Industry 4.0, where the use of data is essential to take decisions in real-time. These data are collected from IoT-situated devices like sensors, machines or systems and used to create smart solutions capable of diagnosing, monitoring or predicting the future. The achieved results can then be incorporated into a system or be accessible from some interactive device (e.g., machine, tablet, mobile, mupi).

This Special Issue wants to explore some trends and innovative and applied solutions that combine data science with IoT to make a system smart. It is an excellent opportunity for researchers, managers, industry, society, and other communities to disseminate their work.

Prof. Dr. Filipe Portela
Guest Editor

Manuscript Submission Information

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Keywords

  • smart cities
  • Internet of Things
  • data science
  • artificial intelligence
  • decision support systems
  • knowledge discovery
  • data systems
  • machine learning
  • Industry 4.0

Published Papers (2 papers)

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Review

31 pages, 602 KiB  
Review
A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks
Future Internet 2023, 15(8), 255; https://doi.org/10.3390/fi15080255 - 30 Jul 2023
Cited by 7 | Viewed by 4477
Abstract
In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient [...] Read more.
In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient deployment of artificial intelligence models and techniques, the view is rapidly changing, with a shift towards machine and deep learning approaches becoming apparent, even without a complete evaluation of the superiority of the new approach over the classic statistical algorithms. Our work constitutes an extensive review of the published scientific literature regarding the comparison of ARIMA and machine learning algorithms applied to time series forecasting problems, as well as the combination of these two approaches in hybrid statistical-AI models in a wide variety of data applications (finance, health, weather, utilities, and network traffic prediction). Our review has shown that the AI algorithms display better prediction performance in most applications, with a few notable exceptions analyzed in our Discussion and Conclusions sections, while the hybrid statistical-AI models steadily outperform their individual parts, utilizing the best algorithmic features of both worlds. Full article
(This article belongs to the Special Issue Smart Data and Systems for the Internet of Things)
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21 pages, 771 KiB  
Review
Disruptive Technologies for Parliaments: A Literature Review
Future Internet 2023, 15(2), 66; https://doi.org/10.3390/fi15020066 - 05 Feb 2023
Cited by 2 | Viewed by 1950
Abstract
Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a new organisational model that affects business procedures [...] Read more.
Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a new organisational model that affects business procedures and processes. Additionally, the use of inclusive participatory organisational models is essential for the effective adoption of these technologies. Such models aim to transform organisational structures, as well. Public organisations, such as the parliament, could utilise information systems’ personalisation techniques. As there are a lot of efforts to define the framework, the methodology, the techniques, the platforms, and the suitable models for digital technologies adoption in public organisations, this paper aims to provide a literature review for disruptive technology inclusive use in parliaments. The review emphasises the assessment of the applicability of the technologies, their maturity and usefulness, user acceptance, their performance, and their correlation to the adoption of relevant innovative, inclusive organisational models. It is argued that the efficient digital transformation of democratic institutions, such as parliaments, with the use of advanced e-governance tools and disruptive technologies, requires strategic approaches for adoption, acceptance, and inclusive service adaptation. Full article
(This article belongs to the Special Issue Smart Data and Systems for the Internet of Things)
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