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Future Internet
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Smart Objects and Technologies for Social Good II
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Smart Objects and Technologies for Social Good II

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (24 June 2023) | Viewed by 8811

Special Issue Editor

Prof. Dr. Ivan Miguel Pires

E-Mail Website
Guest Editor
Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal
Interests: ambient assisted living; data classification; data processing; data fusion; volume; image processing; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to collect papers related to the design, implementation, deployment, operation, and evaluation of smart objects and technologies for social good. Social good includes products and services that benefit several people with special needs (e.g., older adults), the performance of sports, and young people.

These represent key issues in the implementation of different kinds of solutions in different fields, such as healthcare, safety, sports, environment, democracy, computer science, and human rights.

It is a wide field that is a very challenging subject for different research studies, requiring the integration of classical and innovative methodologies. It also promoted the combination of different knowledge from different sciences.

Both theoretical and application-oriented papers on various aspects of traditional and innovative medicine, intelligent systems and devices, distributed computing, artificial intelligence, data acquisition, data processing, diagnostic, preventive medicine, sensored devices, participatory medicine, big data, precision systems, automation, Internet of Things, and cyber-physical systems are invited.

Prof. Dr. Ivan Miguel Pires
Guest Editor

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. 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 1600 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

  • environment sensing, monitoring and preservation
  • health and social care
  • pervasive and ubiquitous services in cloud and IoT
  • smart living and e-health
  • mobile applications and ubiquitous devices in Healthcare and lifestyle training
  • big data analytics for e-health
  • intelligent decision support and data systems in health care, medicine and society
  • cloud computing
  • cyber-physical systems and real-time data collection for social good
  • machine learning applications for social good

Related Special Issue

Published Papers (4 papers)

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Research

29 pages, 4192 KiB  
Article
Post-Digital Learning for Rural Development: A Case Study of Open Biotec MOOCs in Colombia
by Carlos Ocampo-López, Oscar Muñoz-Blandón, Leidy Rendón-Castrillón and Margarita Ramírez-Carmona
Future Internet 2023, 15(4), 141; https://doi.org/10.3390/fi15040141 - 06 Apr 2023
Cited by 1 | Viewed by 2178
Abstract
This research presents an experience of post-digital learning of biotechnology in rural areas in Colombia by implementing a massive open online course (MOOC) for biotechnology education called Open Biotec. The objective was to improve Colombian rural development by creating learning communities around [...] Read more.
This research presents an experience of post-digital learning of biotechnology in rural areas in Colombia by implementing a massive open online course (MOOC) for biotechnology education called Open Biotec. The objective was to improve Colombian rural development by creating learning communities around five topics: waste management, environmental biotechnology, bioprocesses, alternative energies, and bio-entrepreneurship. The study utilized various methods, including a socioeconomic diagnosis of a subregion of the department of Antioquia, Colombia, and the creation of MOOCs using the Action Research methodology. The pilot test of the training route in biotechnology involved the progressive development of the MOOC courses that comprise it. Around 918 students from rural areas were trained, and statistical analysis showed that the average grade of the students increased significantly from 6.13 to 7.53 and the median from 6.15 to 8.00. The study results demonstrate how the learning experience designed in the Open Biotec MOOC increases the degree of knowledge of students in this field of biotechnology, offering an opportunity to establish sustainable learning communities with participation and collaborative action mediated by virtual tools. The study concludes that rural education in Colombia could be strengthened with a training strategy for rural communities supported by MOOCs focused on the responsible use of local biodiversity from a biotechnological perspective. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good II)
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23 pages, 1750 KiB  
Article
Transforming IoT Events to Meaningful Business Events on the Edge: Implementation for Smart Farming Application
by Dimitris Gkoulis, Cleopatra Bardaki, George Kousiouris and Mara Nikolaidou
Future Internet 2023, 15(4), 135; https://doi.org/10.3390/fi15040135 - 31 Mar 2023
Cited by 1 | Viewed by 1410
Abstract
This paper focuses on Internet of Things (IoT) architectures and knowledge generation out of streams of events as the primary elements concerning the creation of user-centric IoT services. We provide a general, symmetrical IoT architecture, which enables two-way bidirectional communication between things and [...] Read more.
This paper focuses on Internet of Things (IoT) architectures and knowledge generation out of streams of events as the primary elements concerning the creation of user-centric IoT services. We provide a general, symmetrical IoT architecture, which enables two-way bidirectional communication between things and users within an application domain. We focus on two main components of the architecture (i.e., Event Engine and Process Engine) that handle event transformation by implementing parametric Complex Event Processing (CEP). More specifically, we describe and implement the transformation cycle of events starting from raw IoT data to their processing and transformation of events for calculating information that we need in an IoT-enabled application context. The implementation includes a library of composite transformations grouping the gradual and sequential steps for transforming basic IoT events into business events, which include ingestion, event splitting, and calculation of measurements’ average value. The appropriateness and possibility of inclusion and integration of the implementation in an IoT environment are demonstrated by providing our implementation for a smart farming application domain with four scenarios that each reflect a user’s requirements. Further, we discuss the quality properties of each scenario. Ultimately, we propose an IoT architecture and, specifically, a parametric CEP model and implementation for future researchers and practitioners who aspire to build IoT applications. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good II)
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17 pages, 9851 KiB  
Article
A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset
by Ahmad Taha, Basel Barakat, Mohammad M. A. Taha, Mahmoud A. Shawky, Chun Sing Lai, Sajjad Hussain, Muhammad Zainul Abideen and Qammer H. Abbasi
Future Internet 2023, 15(4), 134; https://doi.org/10.3390/fi15040134 - 31 Mar 2023
Viewed by 1404
Abstract
Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in [...] Read more.
Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R2 values of 87.20% and 68.06%, respectively. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good II)
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17 pages, 14648 KiB  
Article
Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
by Shadi AlZu’bi, Mohammad Elbes, Ala Mughaid, Noor Bdair, Laith Abualigah, Agostino Forestiero and Raed Abu Zitar
Future Internet 2023, 15(2), 85; https://doi.org/10.3390/fi15020085 - 20 Feb 2023
Cited by 5 | Viewed by 3314
Abstract
Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, [...] Read more.
Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good II)
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