Future Internet

27 pages, 3239 KiB

Open AccessSystematic Review

Does Anyone Care about the Opinion of People on Participating in a “Social” Metaverse? A Review and a Draft Proposal for a Surveying Tool

by Stefano Mottura

Future Internet 2024, 16(7), 236; https://doi.org/10.3390/fi16070236 - 2 Jul 2024

Viewed by 98

Abstract

In recent years, the attention paid to the metaverse in the scientific world has increased; the hottest topics include system architecture and enabling technologies, as well as business, privacy, ethical, and security issues. On the other side, at the mainstream level, it is [...] Read more.

In recent years, the attention paid to the metaverse in the scientific world has increased; the hottest topics include system architecture and enabling technologies, as well as business, privacy, ethical, and security issues. On the other side, at the mainstream level, it is well known that the company Meta (formerly Facebook) is striving to realize its interpretation of a “social” metaverse. As Meta is a big leader of social media, it is reasonable to guess that, in the future, users will participate in a new social platform, such as that which the company is building by depicting unlimited and engaging opportunities. Regardless of Meta, we ask what the opinion of people is about this possible future scenario. A literature search of previous works about this topic has been done; the few results we found were not properly on topic and showed heterogeneous content. A survey on interpretations of the metaverse of major information and communication technologies (ICT) companies that impact the consumer world was undertaken; the results show that Meta is the most prominent company with the mission of building a ”social” metaverse worldwide. Finally, a draft of a tool for assessing the predilection of people for a “social” metaverse, based on various facets of the future social platform, is proposed. Full article

(This article belongs to the Special Issue Virtual Reality and Metaverse: Impact on the Digital Transformation of Society II)

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18 pages, 721 KiB

Open AccessArticle

A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model

by Enbo Sun, Jiaxuan Han, Yiquan Li and Cheng Huang

Future Internet 2024, 16(7), 235; https://doi.org/10.3390/fi16070235 - 2 Jul 2024

Viewed by 169

Abstract

In recent years, various Remote Code Execution vulnerabilities on the Internet have been exposed frequently; thus, more and more security researchers have begun to pay attention to the detection of Remote Code Execution attacks. In this paper, we focus on three kinds of [...] Read more.

In recent years, various Remote Code Execution vulnerabilities on the Internet have been exposed frequently; thus, more and more security researchers have begun to pay attention to the detection of Remote Code Execution attacks. In this paper, we focus on three kinds of common Remote Code Execution attacks: XML External Entity, Expression Language Injection, and Insecure Deserialization. We propose a packet content-oriented Remote Code Execution attack payload detection model. For the XML External Entity attack, we propose an algorithm to construct the use-definition chain of XML entities, and implement detection based on the integrity of the chain and the behavior of the chain’s tail node. For the Expression Language Injection and Insecure Deserialization attack, we extract 34 features to represent the string operation and the use of sensitive classes/methods in the code, and then train a machine learning model to implement detection. At the same time, we build a dataset to evaluate the effect of the proposed model. The evaluation results show that the model performs well in detecting XML External Entity attacks, achieving a precision of 0.85 and a recall of 0.94. Similarly, the model performs well in detecting Expression Language Injection and Insecure Deserialization attacks, achieving a precision of 0.99 and a recall of 0.88. Full article

(This article belongs to the Section Cybersecurity)

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23 pages, 736 KiB

Open AccessReview

Smart Irrigation Systems from Cyber–Physical Perspective: State of Art and Future Directions

by Mian Qian, Cheng Qian, Guobin Xu, Pu Tian and Wei Yu

Future Internet 2024, 16(7), 234; https://doi.org/10.3390/fi16070234 - 29 Jun 2024

Viewed by 202

Abstract

Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The [...] Read more.

Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, develo** countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people’s rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber–physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT’s applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring, autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field. Full article

(This article belongs to the Special Issue 2024 and 2025 Feature Papers from Future Internet’s Editorial Board Members)

20 pages, 3964 KiB

Open AccessArticle

Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling

by Marcus Pieska, Andreas Kassler, Anna Brunstrom, Veselin Rakocevic and Markus Amend

Future Internet 2024, 16(7), 233; https://doi.org/10.3390/fi16070233 - 28 Jun 2024

Viewed by 167

Abstract

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless [...] Read more.

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm. Full article

14 pages, 624 KiB

Open AccessArticle

Digital Transformation in the Construction Sector: Blockchain, BIM and SSI for a More Sustainable and Transparent System

by Luisanna Cocco and Roberto Tonelli

Future Internet 2024, 16(7), 232; https://doi.org/10.3390/fi16070232 - 28 Jun 2024

Viewed by 167

Abstract

This article presents a model built for deep digitalization in the construction industry and for making building information modeling achieve a greater level of transparency, verifiability and effectiveness for the benefit of all stakeholders. Thanks to blockchain and the self-sovereign identity paradigm, the [...] Read more.

This article presents a model built for deep digitalization in the construction industry and for making building information modeling achieve a greater level of transparency, verifiability and effectiveness for the benefit of all stakeholders. Thanks to blockchain and the self-sovereign identity paradigm, the model guarantees data integrity and transaction reliability, enabling the generation of more efficient and productive businesses. The model includes a decentralized application for notarization of the information flow in building information modeling processes; the application is implemented and tested on a local blockchain. The proposed model represents a so-called digital twin and is, hence, a huge system that manages all the information flow associated with a building throughout its life cycle, returning to individuals the control of their own data. In this model, all stakeholders operate based on so-called decentralized identifiers and DID documents, which store on-chain the fingerprints of the information flow in a common data environment. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Decentralized Edge Environments)

16 pages, 1941 KiB

Open AccessSystematic Review

Exploring the Architectural Composition of Cyber Ranges: A Systematic Review

by Dionysios Stamatopoulos, Menelaos Katsantonis, Panagiotis Fouliras and Ioannis Mavridis

Future Internet 2024, 16(7), 231; https://doi.org/10.3390/fi16070231 - 28 Jun 2024

Viewed by 322

Abstract

In light of the ever-increasing complexity of cyber–physical systems (CPSs) and information technology networking systems (ITNs), cyber ranges (CRs) have emerged as a promising solution by providing theoretical and practical cybersecurity knowledge for participants’ skill improvement toward a safe work environment. This research [...] Read more.

In light of the ever-increasing complexity of cyber–physical systems (CPSs) and information technology networking systems (ITNs), cyber ranges (CRs) have emerged as a promising solution by providing theoretical and practical cybersecurity knowledge for participants’ skill improvement toward a safe work environment. This research adds to the extant respective literature, exploring the architectural composition of CRs. It aims to improve the understanding of their design and how they are deployed, expanding skill levels in constructing better CRs. Our research follows the PRISMA methodology guidelines for transparency, which includes a search flow of articles based on specific criteria and quality valuation of selected articles. To extract valuable research datasets, we identify keyword co-occurrences that selected articles are concentrated on. In the context of literature evidence, we identify key attributes and trends, providing details of CRs concerning their architectural composition and underlying infrastructure, along with today’s challenges and future research directions. A total of 102 research articles’ qualitative analyses reveal a lack of adequate architecture examination when CR elements and services interoperate with other CR elements and services participating, leading to gaps that increase the administration burden. We posit that the results of this study can be leveraged as a baseline for future enhancements toward the development of CRs. Full article

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18 pages, 884 KiB

Open AccessArticle

Trusted Composition of Internet of Medical Things over Imperfect Networks

by Ehsan Ahmad, Brian Larson and Abdulbasid Banga

Future Internet 2024, 16(7), 230; https://doi.org/10.3390/fi16070230 - 28 Jun 2024

Viewed by 296

Abstract

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite [...] Read more.

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated. Full article

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23 pages, 886 KiB

Open AccessArticle

Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns

by Lucas Henriques, Felipe Prata Lima and Cecilia Castro

Future Internet 2024, 16(7), 229; https://doi.org/10.3390/fi16070229 - 28 Jun 2024

Viewed by 335

Abstract

Understanding household energy-consumption patterns is essential for develo** effective energy-conservation strategies. This study aims to identify ‘out-profiled’ consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with [...] Read more.

Understanding household energy-consumption patterns is essential for develo** effective energy-conservation strategies. This study aims to identify ‘out-profiled’ consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development. Full article

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19 pages, 2171 KiB

Open AccessArticle

Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics

by Pietro Ruiu, Salvatore Saiu and Enrico Grosso

Future Internet 2024, 16(7), 228; https://doi.org/10.3390/fi16070228 - 28 Jun 2024

Viewed by 412

Abstract

Today, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious [...] Read more.

Today, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious beliefs, financial information, or health status) in an uncontrolled manner. A crucial role in this scenario is played by the weakness of international regulatory frameworks due to the slow response time of legislators who are incapable, from a regulatory point of view, of kee** pace with technological evolution and responding to the new requirements coming from the social context, which is increasingly characterized by the pervasive presence of new technologies, such as smartphones and wearable devices. At the European level, the General Data Protection Regulation (GDPR) and the Regulation on Electronic Identification, Authentication and Trust Services (eIDAS) have marked a significant turning point in the regulatory landscape. However, the mechanisms proposed present clear security issues, particularly in light of emerging concepts such as digital identity. Moreover, despite the centrality of biometric issues within the European regulatory framework and the practical introduction of biometric data within electronic national identity (eID) cards, there are still no efforts to use biometric features for the identification and authentication of a person in a digital context. This paper clarifies and precisely defines the potential impact of biometric-based digital identity and hypothesizes its practical use for accessing network-based services and applications commonly used in daily life. Using the Italian eID card as a model, an authentication scheme leveraging biometric data is proposed, ensuring full compliance with GDPR and eIDAS regulations. The findings suggest that such a scheme can significantly improve the security and reliability of electronic identification systems, promoting broader adoption of eIDAS solutions. Full article

(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—Volume III)

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4 pages, 139 KiB

Open AccessEditorial

Edge Cloud Computing and Federated–Split Learning in Internet of Things

by Qiang Duan and Zhihui Lu

Future Internet 2024, 16(7), 227; https://doi.org/10.3390/fi16070227 - 28 Jun 2024

Viewed by 274

Abstract

The wide deployment of the Internet of Things (IoT) necessitates new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process huge amounts of data [...] Full article

(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in the Internet of Things)

36 pages, 3662 KiB

Open AccessArticle

Enhancing Network Slicing Security: Machine Learning, Software-Defined Networking, and Network Functions Virtualization-Driven Strategies

by José Cunha, Pedro Ferreira, Eva M. Castro, Paula Cristina Oliveira, Maria João Nicolau, Iván Núñez, Xosé Ramon Sousa and Carlos Serôdio

Future Internet 2024, 16(7), 226; https://doi.org/10.3390/fi16070226 - 27 Jun 2024

Viewed by 452

Abstract

The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical [...] Read more.

The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI’s predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks. Full article

(This article belongs to the Special Issue Privacy and Security in Computing Continuum and Data-Driven Workflows)

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28 pages, 1675 KiB

Open AccessReview

Building Information Modeling and Digital Twins for Functional and Technical Design of Smart Buildings with Distributed IoT Networks—Review and New Challenges Discussion

by Gabriela Walczyk and Andrzej Ożadowicz

Future Internet 2024, 16(7), 225; https://doi.org/10.3390/fi16070225 - 27 Jun 2024

Viewed by 199

Abstract

Modern building automation systems implement plenty of advanced control and monitoring functions that consider various parameters like users’ activity, lighting, temperature changes, etc. Moreover, novel solutions based on the Internet of Things and cloud services are also being developed for smart buildings to [...] Read more.

Modern building automation systems implement plenty of advanced control and monitoring functions that consider various parameters like users’ activity, lighting, temperature changes, etc. Moreover, novel solutions based on the Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements, and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a novel approach in building automation systems design to provide effective implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. This paper discusses the development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with the Internet of Things, considering the organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements set out in the EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator. Full article

(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)

17 pages, 420 KiB

Open AccessArticle

Evaluating Quantized Llama 2 Models for IoT Privacy Policy Language Generation

by Bhavani Malisetty and Alfredo J. Perez

Future Internet 2024, 16(7), 224; https://doi.org/10.3390/fi16070224 - 26 Jun 2024

Viewed by 477

Abstract

Quantized large language models are large language models (LLMs) optimized for model size while preserving their efficacy. They can be executed on consumer-grade computers without the powerful features of dedicated servers needed to execute regular (non-quantized) LLMs. Because of their ability to summarize, [...] Read more.

Quantized large language models are large language models (LLMs) optimized for model size while preserving their efficacy. They can be executed on consumer-grade computers without the powerful features of dedicated servers needed to execute regular (non-quantized) LLMs. Because of their ability to summarize, answer questions, and provide insights, LLMs are being used to analyze large texts/documents. One of these types of large texts/documents are Internet of Things (IoT) privacy policies, which are documents specifying how smart home gadgets, health-monitoring wearables, and personal voice assistants (among others) collect and manage consumer/user data on behalf of Internet companies providing services. Even though privacy policies are important, they are difficult to comprehend due to their length and how they are written, which makes them attractive for analysis using LLMs. This study evaluates how quantized LLMs are modeling the language of privacy policies to be potentially used to transform IoT privacy policies into simpler, more usable formats, thus aiding comprehension. While the long-term goal is to achieve this usable transformation, our work focuses on evaluating quantized LLM models used for IoT privacy policy language. Particularly, we study 4-bit, 5-bit, and 8-bit quantized versions of the large language model Meta AI version 2 (Llama 2) and the base Llama 2 model (zero-shot, without fine-tuning) under different metrics and prompts to determine how well these quantized versions model the language of IoT privacy policy documents by completing and generating privacy policy text. Full article

(This article belongs to the Special Issue Privacy and Security in Computing Continuum and Data-Driven Workflows)

21 pages, 3539 KiB

Open AccessArticle

Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric Visibility

by Aris Cahyadi Risdianto, Muhammad Usman and Muhammad Ahmad Rathore

Future Internet 2024, 16(7), 223; https://doi.org/10.3390/fi16070223 - 25 Jun 2024

Viewed by 515

Abstract

The Internet architecture has recently shifted towards a framework characterized by multiple interconnected cloud sites, all linked via an L3 IP network. With this shift, managing networking controls among multiple cloud sites is becoming a significant operational challenge. In particular, ensuring effective networking [...] Read more.

The Internet architecture has recently shifted towards a framework characterized by multiple interconnected cloud sites, all linked via an L3 IP network. With this shift, managing networking controls among multiple cloud sites is becoming a significant operational challenge. In particular, ensuring effective networking control necessitates a deeper understanding of flow-level dynamics for comprehensively monitoring interconnection statuses across multiple sites. In this paper, we first propose an IO Visor-enabled tracing solution for Linux-based boxes to efficiently enable the comprehensive collection of network packet flows across interconnected sites. Next, we apply IP prefix-based flow-level analysis at a centralized location to support the intent-based networking control application. This flow-level analysis involves generating policy-based specific action (i.e., redirect) via SDN controllers for specific source IP prefixes, which are causing unknown or potentially vulnerable flows. Furthermore, we employ an open-source ONOS SDN controller to tackle the challenge of managing the hybrid SDN-IP interconnections. By leveraging intent-based networking control, we effectively apply ONOS intents based on IP routing information and generated a set of forwarding action. We evaluate our proposed solution in an experimental SDN-cloud testbed, demonstrating its effectiveness in real-world scenarios. Overall, through the seamless integration of these monitoring and control approaches, we manage to enhance the adaptability and security of the interconnected cloud sites of the testbed. Full article

16 pages, 729 KiB

Open AccessArticle

INFLUTRUST: Trust-Based Influencer Marketing Campaigns in Online Social Networks

by Adedamola Adesokan, Aisha B Rahman and Eirini Eleni Tsiropoulou

Future Internet 2024, 16(7), 222; https://doi.org/10.3390/fi16070222 - 25 Jun 2024

Viewed by 213

Abstract

This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning [...] Read more.

This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms. Full article

(This article belongs to the Special Issue 2024 and 2025 Feature Papers from Future Internet’s Editorial Board Members)

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34 pages, 3880 KiB

Open AccessReview

Map** How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

by Loukas Triantafyllopoulos, Evgenia Paxinou, Georgios Feretzakis, Dimitris Kalles and Vassilios S. Verykios

Future Internet 2024, 16(7), 221; https://doi.org/10.3390/fi16070221 - 23 Jun 2024

Viewed by 494

Abstract

The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed [...] Read more.

The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI’s current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

20 pages, 596 KiB

Open AccessArticle

Differential Private Federated Learning in Geographically Distributed Public Administration Processes

by Mirwais Ahmadzai and Giang Nguyen

Future Internet 2024, 16(7), 220; https://doi.org/10.3390/fi16070220 - 23 Jun 2024

Viewed by 271

Abstract

Public administration frequently deals with geographically scattered personal data between multiple government locations and organizations. As digital technologies advance, public administration is increasingly relying on collaborative intelligence while protecting individual privacy. In this context, federated learning has become known as a potential technique [...] Read more.

Public administration frequently deals with geographically scattered personal data between multiple government locations and organizations. As digital technologies advance, public administration is increasingly relying on collaborative intelligence while protecting individual privacy. In this context, federated learning has become known as a potential technique to train machine learning models on private and distributed data while maintaining data privacy. This work looks at the trade-off between privacy assurances and vulnerability to membership inference attacks in differential private federated learning in the context of public administration applications. Real-world data from collaborating organizations, concretely, the payroll data from the Ministry of Education and the public opinion survey data from Asia Foundation in Afghanistan, were used to evaluate the effectiveness of noise injection, a typical defense strategy against membership inference attacks, at different noise levels. The investigation focused on the impact of noise on model performance and selected privacy metrics applicable to public administration data. The findings highlight the importance of a balanced compromise between data privacy and model utility because excessive noise can reduce the accuracy of the model. They also highlight the need for careful consideration of noise levels in differential private federated learning for public administration tasks to provide a well-calibrated balance between data privacy and model utility, contributing toward transparent government practices. Full article

(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)

23 pages, 732 KiB

Open AccessSystematic Review

A Systematic Review and Comprehensive Analysis of Pioneering AI Chatbot Models from Education to Healthcare: ChatGPT, Bard, Llama, Ernie and Grok

by Ketmanto Wangsa, Shakir Karim, Ergun Gide and Mahmoud Elkhodr

Future Internet 2024, 16(7), 219; https://doi.org/10.3390/fi16070219 - 22 Jun 2024

Viewed by 312

Abstract

AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and [...] Read more.

AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies. Full article

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Future Internet, Volume 16, Issue 7 (July 2024) – 18 articles

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