Future Internet

2024

Jump to: 2023, 2022, 2021

32 pages, 6053 KiB

Open AccessArticle

Are Strong Baselines Enough? False News Detection with Machine Learning

by Lara Aslan, Michal Ptaszynski and Jukka Jauhiainen

Future Internet 2024, 16(9), 322; https://doi.org/10.3390/fi16090322 - 5 Sep 2024

Viewed by 3952

Abstract

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news [...] Read more.

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news and the various ways of automatically detecting it, this article provides its own experiments. These experiments were carried out on four different datasets, one that was made just for this article, using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models. Full article

► Show Figures

Figure 1

23 pages, 2046 KiB

Open AccessArticle

The Digital Footprints on the Run: A Forensic Examination of Android Running Workout Applications

by Fabian Nunes, Patrício Domingues and Miguel Frade

Future Internet 2024, 16(9), 304; https://doi.org/10.3390/fi16090304 - 23 Aug 2024

Viewed by 679

Abstract

This study applies a forensic examination to six distinct Android fitness applications centered around monitoring running activities. The applications are Adidas Running, MapMyWalk, Nike Run Club, Pumatrac, Runkeeper and Strava. Specifically, we perform a post mortem analysis of each application to find and [...] Read more.

This study applies a forensic examination to six distinct Android fitness applications centered around monitoring running activities. The applications are Adidas Running, MapMyWalk, Nike Run Club, Pumatrac, Runkeeper and Strava. Specifically, we perform a post mortem analysis of each application to find and document artifacts such as timelines and Global Positioning System (GPS) coordinates of running workouts that could prove helpful in digital forensic investigations. First, we focused on the Nike Run Club application and used the gained knowledge to analyze the other applications, taking advantage of their similarity. We began by creating a test environment and using each application during a fixed period. This procedure allowed us to gather testing data, and, to ensure access to all data generated by the apps, we used a rooted Android smartphone. For the forensic analysis, we examined the data stored by the smartphone application and documented the forensic artifacts found. To ease forensic data processing, we created several Python modules for the well-known Android Logs Events And Protobuf Parser (ALEAPP) digital forensic framework. These modules process the data sources, creating reports with the primary digital artifacts, which include the workout activities and related GPS data. Full article

► Show Figures

Figure 1

29 pages, 521 KiB

Open AccessReview

A Survey on the Use of Large Language Models (LLMs) in Fake News

by Eleftheria Papageorgiou, Christos Chronis, Iraklis Varlamis and Yassine Himeur

Future Internet 2024, 16(8), 298; https://doi.org/10.3390/fi16080298 - 19 Aug 2024

Cited by 1 | Viewed by 7588

Abstract

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall [...] Read more.

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information. Full article

► Show Figures

Figure 1

14 pages, 3949 KiB

Open AccessArticle

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

by Rui Zhao, Jutao Hao and Huan Huo

Future Internet 2024, 16(6), 194; https://doi.org/10.3390/fi16060194 - 31 May 2024

Viewed by 860

Abstract

In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal [...] Read more.

In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal daytime lighting, visibility is enhanced, leading to superior pedestrian detection outcomes. Conversely, under low-light conditions, visible light mode imaging falters due to the inadequate provision of pedestrian target information, resulting in a marked decline in detection efficacy. In this context, infrared light mode imaging emerges as a valuable supplement, bolstering pedestrian information provision. This paper delves into pedestrian detection and tracking algorithms within a multi-modal image framework grounded in deep learning methodologies. Leveraging the YOLOv4 algorithm as a foundation, augmented by a channel stack fusion module, a novel multi-modal pedestrian detection algorithm tailored for intelligent transportation is proposed. This algorithm capitalizes on the fusion of visible and infrared light mode image features to enhance pedestrian detection performance amidst complex road environments. Experimental findings demonstrate that compared to the Visible-YOLOv4 algorithm, renowned for its high performance, the proposed Double-YOLOv4-CSE algorithm exhibits a notable improvement, boasting a 5.0% accuracy rate enhancement and a 6.9% reduction in logarithmic average missing rate. This research’s goal is to ensure that the algorithm can run smoothly even on a low configuration 1080 Ti GPU and to improve the algorithm’s coverage at the application layer, making it affordable and practical for both urban and rural areas. This addresses the broader research problem within the scope of smart cities and remote ends with limited computational power. Full article

► Show Figures

Figure 1

19 pages, 563 KiB

Open AccessArticle

Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills

by Albert Weichselbraun, Norman Süsstrunk, Roger Waldvogel, André Glatzl, Adrian M. P. Braşoveanu and Arno Scharl

Future Internet 2024, 16(5), 144; https://doi.org/10.3390/fi16050144 - 23 Apr 2024

Cited by 1 | Viewed by 2341

Abstract

Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to [...] Read more.

Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to estimate two indicators of high relevance for a skill’s future readiness: its automatability and offshorability. Based on gold standard data, we evaluate the performance of Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier for propagating expert and literature assessments on these indicators of yet unseen skills. The presented approach uses short bipartite skill labels that contain a skill topic (e.g., “Java”) and a corresponding verb (e.g., “programming”) to describe the skill. Classifiers thus need to base their judgments solely on these two input terms. Comprehensive experiments on skewed and balanced datasets show that, in this low-token setting, classifiers benefit from pre-training and fine-tuning and that increased classifier complexity does not yield further improvements. Full article

► Show Figures

Graphical abstract

2023

Jump to: 2024, 2022, 2021

22 pages, 6279 KiB

Open AccessArticle

Fast Way to Predict Parking Lots Availability: For Shared Parking Lots Based on Dynamic Parking Fee System

by Sheng-Ming Wang and Wei-Min Cheng

Future Internet 2023, 15(3), 89; https://doi.org/10.3390/fi15030089 - 22 Feb 2023

Cited by 1 | Viewed by 2190

Abstract

This study mainly focuses on the estimation calculation of urban parking space. Urban parking has always been a problem that plagues governments worldwide. Due to limited parking space, if the parking space is not controlled correctly, with the city’s development, the city will [...] Read more.

This study mainly focuses on the estimation calculation of urban parking space. Urban parking has always been a problem that plagues governments worldwide. Due to limited parking space, if the parking space is not controlled correctly, with the city’s development, the city will eventually face the result that there is nowhere to park. In order to effectively manage the urban parking problem, using the dynamic parking fee pricing mechanism combined with the concept of shared parking is an excellent way to alleviate the parking problem, but how to quickly estimate the total number of available parking spaces in the area is a big problem. This study provides a fast parking space estimation method and verifies the feasibility of this estimation method through actual data from various types of fields. This study also comprehensively discusses the changing characteristics of parking space data in multiple areas and possible data anomalies and studies and explains the causes of data anomalies. The study also concludes with a description of potential applications of the predictive model in conjunction with subsequent dynamic parking pricing mechanisms and self-driving systems. Full article

► Show Figures

Figure 1

12 pages, 1103 KiB

Open AccessArticle

Adapting Recommendations on Environmental Education Programs

by Katerina Kabassi, Anastasia Papadaki and Athanasios Botonis

Future Internet 2023, 15(1), 28; https://doi.org/10.3390/fi15010028 - 4 Jan 2023

Cited by 1 | Viewed by 1718

Abstract

Stakeholders in Environmental Education (EE) often face difficulties identifying and selecting programs that best suit their needs. This is due, in part, to the lack of expertise in evaluation knowledge and practice, as well as to the absence of a unified database of [...] Read more.

Stakeholders in Environmental Education (EE) often face difficulties identifying and selecting programs that best suit their needs. This is due, in part, to the lack of expertise in evaluation knowledge and practice, as well as to the absence of a unified database of Environmental Education Programs (EEPs) with a defined structure. This article presents the design and development of a web application for evaluating and selecting EEPs. The certified users of the application can insert, view, and evaluate the registered EEPs. At the same time, the application creates and maintains for each user an individual and dynamic user model reflecting their personal preferences. Finally, using all the above information and applying a combination of Multi-Criteria Decision-Making Methods (MCDM), the application provides a comparative and adaptive evaluation in order to help each user to select the EEPs that best suit his/her needs. The personalized recommendations are based on the information about the user stored in the user model and the results of the EEPs evaluations by the users that have applied them. As a case study, we used the EEPs from the Greek Educational System. Full article

► Show Figures

Figure 1

2022

Jump to: 2024, 2023, 2021

19 pages, 8158 KiB

Open AccessArticle

A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams

by Barbara Cardone, Ferdinando Di Martino and Vittorio Miraglia

Future Internet 2023, 15(1), 23; https://doi.org/10.3390/fi15010023 - 30 Dec 2022

Cited by 4 | Viewed by 2172

Abstract

Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied [...] Read more.

Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents’ postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant/pleasant emotions prevail over the analyzed time period are labeled as hot/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens’ discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population/building density. On the contrary, cold spots cover urban areas having a lower population density. Full article

► Show Figures

Figure 1

22 pages, 1302 KiB

Open AccessArticle

COVID-Related Misinformation Migration to BitChute and Odysee

by Olga Papadopoulou, Evangelia Kartsounidou and Symeon Papadopoulos

Future Internet 2022, 14(12), 350; https://doi.org/10.3390/fi14120350 - 23 Nov 2022

Cited by 6 | Viewed by 8194

Abstract

The overwhelming amount of information and misinformation on social media platforms has created a new role that these platforms are inclined to take on, that of the Internet custodian. Mainstream platforms, such as Facebook, Twitter and YouTube, are under tremendous public and political [...] Read more.

The overwhelming amount of information and misinformation on social media platforms has created a new role that these platforms are inclined to take on, that of the Internet custodian. Mainstream platforms, such as Facebook, Twitter and YouTube, are under tremendous public and political pressure to combat disinformation and remove harmful content. Meanwhile, smaller platforms, such as BitChute and Odysee, have emerged and provide fertile ground for disinformation as a result of their low content-moderation policy. In this study, we analyze the phenomenon of removed content migration from YouTube to BitChute and Odysee. In particular, starting from a list of COVID-related videos removed from YouTube due to violating its misinformation policy, we find that ∼15% (1114 videos) of them migrated to the two low content-moderation platforms under study. This amounts to 4096 videos on BitChute and 1810 on Odysee. We present an analysis of this video dataset, revealing characteristics of misinformation dissemination similar to those on YouTube and other mainstream social media platforms. The BitChute–Odysee COVID-related dataset is publicly available for research purposes on misinformation analysis. Full article

► Show Figures

Figure 1

55 pages, 32303 KiB

Open AccessArticle

Evaluation of the Factors That Impact the Perception of Online Content Trustworthiness by Income, Political Affiliation and Online Usage Time

by Matthew Spradling and Jeremy Straub

Future Internet 2022, 14(11), 320; https://doi.org/10.3390/fi14110320 - 3 Nov 2022

Viewed by 2452

Abstract

Intentionally deceptive online content represents a significant issue for society. Multiple techniques have been proposed to identify and combat its spread. To understand how to inform individuals most effectively about the potential biases of and other issues with content, this article studies factors [...] Read more.

Intentionally deceptive online content represents a significant issue for society. Multiple techniques have been proposed to identify and combat its spread. To understand how to inform individuals most effectively about the potential biases of and other issues with content, this article studies factors that impact the perception of online content. Specifically, it looks at how these factors have similar or different impact depending on the income level, political affiliation and online usage time of Americans. A national survey was conducted that asked respondents about multiple factors that influence their and others’ perception of online content trustworthiness. It also asked what the ideal impact of these factors should be. This data is presented and analyzed herein, conclusions are drawn and their implications, with regard to preventing the spread of deceptive online content, are discussed. Full article

► Show Figures

Figure 1

17 pages, 3404 KiB

Open AccessArticle

Low Power Blockchained E-Vote Platform for University Environment

by Faten Chaabane, Jalel Ktari, Tarek Frikha and Habib Hamam

Future Internet 2022, 14(9), 269; https://doi.org/10.3390/fi14090269 - 19 Sep 2022

Cited by 19 | Viewed by 3144

Abstract

With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected [...] Read more.

With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected people. One of the remedies to this is precaution, and particularly social distancing. In the same vein, this paper proposes a remote voting system, which has to be secure, anonymous, irreversible, accessible, and simple to use. It therefore allows voters to have the possibility to vote for their candidate without having to perform the operation on site. This system will be used for university elections and particularly for student elections. We propose a platform based on a decentralized system. This system will use two blockchains communicating with each other: the public Ethereum blockchain and the private Quorum blockchain. The private blockchain will be institution-specific. All these blockchains send the necessary data to the public blockchain which manages different data related to the universities and the ministry. This system enables using encrypted data with the SHA-256 algorithm to have both security and information security. Motivated by the high energy consumption of blockchain and by the performance improvements in low-power, a test is performed on a low-power embedded platform Raspberry PI4 showing the possibility to use the Blockchain with limited resources. Full article

► Show Figures

Graphical abstract

35 pages, 9433 KiB

Open AccessArticle

A Smart Parking Solution by Integrating NB-IoT Radio Communication Technology into the Core IoT Platform

by Esad Kadusic, Natasa Zivic, Christoph Ruland and Narcisa Hadzajlic

Future Internet 2022, 14(8), 219; https://doi.org/10.3390/fi14080219 - 25 Jul 2022

Cited by 15 | Viewed by 4984

Abstract

With the emerging Internet of Things (IoT) technologies, the smart city paradigm has become a reality. Wireless low-power communication technologies (LPWAN) are widely used for device connection in smart homes, smart lighting, mitering, and so on. This work suggests a new approach to [...] Read more.

With the emerging Internet of Things (IoT) technologies, the smart city paradigm has become a reality. Wireless low-power communication technologies (LPWAN) are widely used for device connection in smart homes, smart lighting, mitering, and so on. This work suggests a new approach to a smart parking solution using the benefits of narrowband Internet of Things (NB-IoT) technology. NB-IoT is an LPWAN technology dedicated to sensor communication within 5G mobile networks. This paper proposes the integration of NB-IoT into the core IoT platform, enabling direct sensor data navigation to the IoT radio stations for processing, after which they are forwarded to the user application programming interface (API). Showcasing the results of our research and experiments, this work suggests the ability of NB-IoT technology to support geolocation and navigation services, as well as payment and reservation services for vehicle parking to make the smart parking solutions smarter. Full article

► Show Figures

Figure 1

24 pages, 4895 KiB

Open AccessArticle

Blockchain for Doping Control Applications in Sports: A Conceptual Approach

by Flavio Pinto, Yogachandran Rahulamathavan and James Skinner

Future Internet 2022, 14(7), 210; https://doi.org/10.3390/fi14070210 - 14 Jul 2022

Cited by 4 | Viewed by 3006

Abstract

Doping is a well-known problem in competitive sports. Along the years, several cases have come to public, evidencing corrupt practices from within the sports environment. To guarantee fair play and prevent public health issues, anti-doping organizations and sports authorities are expected to cooperate [...] Read more.

Doping is a well-known problem in competitive sports. Along the years, several cases have come to public, evidencing corrupt practices from within the sports environment. To guarantee fair play and prevent public health issues, anti-doping organizations and sports authorities are expected to cooperate in the fight against doping. To achieve this mission, doping-related data must be produced, stored, accessed, and shared in a secure, tamperproof, and privacy-preserving manner. This paper investigates the processes and tools established by the World Anti-Doping Agency for the global harmonization of doping control activities. From this investigation, it is possible to conclude that there is an inherent trust problem, in part due to a centralized data management paradigm and to the lack of fully digitalized processes. Therefore, this paper presents two main contributions: the concept of a multiorganizational decentralized data governance model and a blockchain-based design for one of the most sensitive data-sharing processes within the anti-doping ecosystem. Throughout this article, it is shown that the adoption of a permissioned blockchain can benefit the whole anti-doping community, creating more reliable processes for handling data, where privacy and security are enhanced. Full article

► Show Figures

Figure 1

16 pages, 8595 KiB

Open AccessArticle

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

by Thanassis Mpimis, Theodore T. Kapsis, Athanasios D. Panagopoulos and Vassilis Gikas

Future Internet 2022, 14(7), 195; https://doi.org/10.3390/fi14070195 - 27 Jun 2022

Cited by 4 | Viewed by 2082

Abstract

Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning [...] Read more.

Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed. Full article

► Show Figures

Figure 1

20 pages, 1069 KiB

Open AccessArticle

A System Proposal for Information Management in Building Sector Based on BIM, SSI, IoT and Blockchain

by Luisanna Cocco, Roberto Tonelli and Michele Marchesi

Future Internet 2022, 14(5), 140; https://doi.org/10.3390/fi14050140 - 30 Apr 2022

Cited by 12 | Viewed by 3793

Abstract

This work presents a Self Sovereign Identity based system proposal to show how Blockchain, Building Information Modeling, Internet of Thing devices, and Self Sovereign Identity concepts can support the process of building digitalization, guaranteeing the compliance standards and technical regulations. The proposal ensures [...] Read more.

This work presents a Self Sovereign Identity based system proposal to show how Blockchain, Building Information Modeling, Internet of Thing devices, and Self Sovereign Identity concepts can support the process of building digitalization, guaranteeing the compliance standards and technical regulations. The proposal ensures eligibility, transparency and traceability of all information produced by stakeholders, or generated by IoT devices appropriately placed, during the entire life cycle of a building artifact. By exploiting the concepts of the Self Sovereign Identity, our proposal allows the identification of all involved stakeholders, the storage off-chain of all information, and that on-chain of the sole data necessary for the information notarization and certification, adopting multi-signature approval mechanisms where appropriate. In addition it allows the eligibility verification of the certificated information, providing also useful information for facility management. It is proposed as an innovative system and companies that adopt the Open Innovation paradigm might want to pursue it. The model proposal is designed exploiting the Veramo platform, hence the Ethereum Blockchain, and all the recommendations about Self Sovereign Identity systems given by the European Blockchain Partnership, and by the World Wide Web Consortium. Full article

► Show Figures

Figure 1

17 pages, 1532 KiB

Open AccessArticle

Using Satellite Imagery to Improve Local Pollution Models for High-Voltage Transmission Lines and Insulators

by Peter Krammer, Marcel Kvassay, Ján Mojžiš, Martin Kenyeres, Miloš Očkay, Ladislav Hluchý, Ľuboš Pavlov and Ľuboš Skurčák

Future Internet 2022, 14(4), 99; https://doi.org/10.3390/fi14040099 - 23 Mar 2022

Cited by 8 | Viewed by 2894

Abstract

This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to [...] Read more.

This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model). Full article

► Show Figures

Graphical abstract

27 pages, 1227 KiB

Open AccessEditor’s ChoiceArticle

A Survey on Intrusion Detection Systems for Fog and Cloud Computing

by Victor Chang, Lewis Golightly, Paolo Modesti, Qianwen Ariel Xu, Le Minh Thao Doan, Karl Hall, Sreeja Boddu and Anna Kobusińska

Future Internet 2022, 14(3), 89; https://doi.org/10.3390/fi14030089 - 13 Mar 2022

Cited by 53 | Viewed by 9988

Abstract

The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there [...] Read more.

The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area. Full article

► Show Figures

Figure 1

18 pages, 731 KiB

Open AccessReview

Business Models for the Internet of Services: State of the Art and Research Agenda

by Jacqueline Zonichenn Reis, Rodrigo Franco Gonçalves, Marcia Terra da Silva and Nikolai Kazantsev

Future Internet 2022, 14(3), 74; https://doi.org/10.3390/fi14030074 - 25 Feb 2022

Cited by 5 | Viewed by 4381

Abstract

The relevance of the Internet of Services (IoS) comes from the global reach of the Internet into everyone’s home and daily activities and from the move from a manufacturing-based economy to a service-based economy. The IoS is seen as a new ecosystem where [...] Read more.

The relevance of the Internet of Services (IoS) comes from the global reach of the Internet into everyone’s home and daily activities and from the move from a manufacturing-based economy to a service-based economy. The IoS is seen as a new ecosystem where service providers and consumers explore their business networks for service provision and consumption. The scientific literature refers to IoS as an important cornerstone for Industry 4.0 and Future Internet; thus, it becomes relevant to study how IoS interacts with business models. Nevertheless, there is a lack of clarity on such an intersection. Moreover, a systematic review of IoS-based business models is still missing. This paper aims to make a systematic review of IoS-based business models and their application fields. We included studies from Scopus and Web of Science databases, we excluded duplicated papers and short conference versions of the later full paper journal publications. Twenty-three different studies are presented, categorized in the sub-areas of IoS, and then by the fields of applications. The main finding highlights the opportunities of IoS applications in different fields, offering directions for future research on this new arena. Full article

► Show Figures

Figure 1

19 pages, 3440 KiB

Open AccessArticle

An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces

by Cunwei Yang, Weiqing Wang, Fengying Li and Degang Yang

Future Internet 2022, 14(2), 40; https://doi.org/10.3390/fi14020040 - 26 Jan 2022

Cited by 7 | Viewed by 3681

Abstract

To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. [...] Read more.

To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic; instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer” algorithm, providing an infection risk assessment for individuals in potential contact with patients. Full article

► Show Figures

Figure 1

2021

Jump to: 2024, 2023, 2022

16 pages, 9390 KiB

Open AccessArticle

A BIM-Based Smart System for Fire Evacuation

by Rania Wehbe and Isam Shahrour

Future Internet 2021, 13(9), 221; https://doi.org/10.3390/fi13090221 - 25 Aug 2021

Cited by 33 | Viewed by 6919

Abstract

Building fires constitute a significant threat that affects property, the environment, and human health. The management of this risk requires an efficient fire evacuation system for buildings’ occupants. Therefore, a smart fire evacuation system that combines building information modeling (BIM) and smart technologies [...] Read more.

Building fires constitute a significant threat that affects property, the environment, and human health. The management of this risk requires an efficient fire evacuation system for buildings’ occupants. Therefore, a smart fire evacuation system that combines building information modeling (BIM) and smart technologies is proposed. The system provides the following capacities: (i) early fire detection; (ii) the evaluation of environmental data; (iii) the identification of the best evacuation path; and (iv) information for occupants about the best evacuation routes. The system was implemented in a research building at Lille University in France. The results show the system’s capacities and benefits, particularly for the identification of the best evacuation paths. Full article

► Show Figures

Figure 1

13 pages, 4157 KiB

Open AccessArticle

Post-Materialist Values of Smart City Societies: International Comparison of Public Values for Good Enough Governance

by Seng Boon Lim, Jalaluddin Abdul Malek and Tan Yigitcanlar

Future Internet 2021, 13(8), 201; https://doi.org/10.3390/fi13080201 - 3 Aug 2021

Cited by 11 | Viewed by 3837

Abstract

This study aims to analyze the application of good enough governance in considering the citizens’ value propositions that shape smart city societies. This paper applied a quantitative method with cross-country comparisons. Survey data were derived from the World Values Survey. Malaysia was chosen [...] Read more.

This study aims to analyze the application of good enough governance in considering the citizens’ value propositions that shape smart city societies. This paper applied a quantitative method with cross-country comparisons. Survey data were derived from the World Values Survey. Malaysia was chosen as the main study area, and compared with Indonesia and other countries worldwide. The findings revealed that politics is the value of least concern across all samples. In terms of qualities for children to develop, respondents in both Malaysia and Indonesia were less concerned about imagination and unselfishness. As for materialist versus post-materialist, the ratios of Malaysia and Indonesia were slightly higher than the average; the post-materialist value of free speech was the lowest value chosen. In the long term, all countries are experiencing the trend of moving toward post-materialist societies. To be sustained under the Collective and Adaptive System of smart city societies, good enough governance in Malaysia and Indonesia should consider the cultural context of the Muslim majority, prioritize governance content that allows more space for political participation and free speech, and cultivate the imagination and unselfishness of children. The generated insights underline the critical role that smart societies play in establishing smart cities. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Innovative People-Centered Solutions Applied to Industries, Cities and Societies

Share This Topical Collection

Editors

Topical Collection Information

Keywords

Published Papers (21 papers)

2024

Jump to: 2023, 2022, 2021

2023

Jump to: 2024, 2022, 2021

2022

Jump to: 2024, 2023, 2021

2021

Jump to: 2024, 2023, 2022

Planned Papers

Further Information

Guidelines

MDPI Initiatives

Follow MDPI