Future Internet

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

Yuzhu Zhang

Hao Xu

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI’s ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We then briefly discuss some associated issues, such as trust, veracity, clinical safety and reliability, privacy, copyrights, ownership, and opportunities, e.g., AI-driven conversational user interfaces for friendlier human-computer interaction. We conclude that generative AI will play an increasingly important role in medicine and healthcare as it further evolves and gets better tailored to the unique settings and requirements of the medical domain and as the laws, policies and regulatory frameworks surrounding its use start taking shape.

Peng Zhang

Maged N. Kamel Boulos

Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges

This review gives an overview of the societal inequalities faced by people with intellectual disabilities, before focusing specifically on challenges people face accessing the Internet. Current access will be outlined along with the societal, support and attitudinal factors that can hinder access. Discussion of carer views of Internet use by people with intellectual disabilities will be covered incorporating consideration of the tension between protection, self-determination and lifestyle issues and gaining Internet access. We will address how impairment related factors may impede access and subsequently discuss how supports may be used to obfuscate impairments and facilitate access. We will move on from this to critically describe some of the potential benefits the Internet could provide to people with intellectual disabilities, including the potential for self-expression, advocacy and developing friendships. Finally, strategies to better include people with intellectual disabilities online will be given along with future research suggestions.

Darren Chadwick

Caroline Wesson

Chris Fullwood

Internet Access by People with Intellectual Disabilities: Inequalities and Opportunities

智力障碍人士的互联网接入：不平等与机遇

It is of great significance for enterprises’ development to effectively use mobile Internet to carry out information feed advertising. This paper aims to study the influence factors and effect of the users’ acceptance intention of information feed advertising through empirical analysis to provide references for further optimizing information feed advertising strategy. Traditional quantitative analysis methods, such as the Structural Equation Model (SEM), can only measure a single factor’s influence from an individual perspective. Therefore, we introduce the Qualitative Comparative Analysis (QCA) and present a two-stage hybrid method based on SEM and QCA. In the first stage, we analyze the influence of a single variable on the acceptance intention of information feed advertising by SEM. Then, in the second stage, we analyze the impact of different variable combinations by QCA. Based on the actual questionnaire data, we define six independent variables and use AMOS, SPSS, and fsQCA to carry out SEM analysis and the fuzzy-set-based QCA analysis, respectively. The SEM analysis results show that the four factors (including consistency, informativeness, sociability, and advertising reward) have a significant positive impact on user acceptance of information feed advertising. On the contrary, perceived advertising clustering has a significant negative impact. In addition, accuracy has no considerable effect. The QCA analysis reveals that seven constructs of six variables can all significantly enhance information feed ads’ acceptance intention.

Jie Zhao

Can Yan

User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA

信息流广告的用户接受度：基于SEM和QCA的混合方法研究

Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions.

Lang Wu

Weijian Ruan

Jinhui Hu

Yaobin He

A Survey on Blockchain-Based Federated Learning

基于区块链的联邦学习综述

Cloud computing and High-Performance Computing (HPC) have become emerging paradigms that drive innovation and reshape the landscape of Information and Communication Technology (ICT) [...]

Jerry Chou

Wu-Chun Chung

Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet

云计算与高性能计算（HPC）在下一代互联网中的进展

Multipath transmission in ICN provides high transmission efficiency and stability. In an IP-ICN compatible network environment, unmodified IP terminal devices can access ICN through gateways, benefiting from these performance enhancements. This paper proposes a gateway framework for hybrid IP-ICN multipath transmission systems, enabling protocol conversion and quality of service management. A packet reordering module is integrated at the egress gateway to address complex packet disorder issues caused by ICN multipath transmission, thereby enhancing the service quality provided to IP terminals. A Reordering Buffer Management Method (RBMM) is introduced, consisting of two key components. First, RBMM employs an improved dynamic threshold scheme for reserved buffer partitioning, efficiently identifying congestion and optimizing buffer resource utilization. Second, a flow-priority-based replacement strategy is designed to enhance fairness in resource allocation by evicting packets with lower delivery probability during congestion. Experimental results demonstrate that RBMM dynamically adapts to varying traffic conditions, maintaining high transmission performance while reducing buffer resource consumption. In comparison to existing methods, RBMM significantly reduces queuing delay and flow completion time, providing more balanced resource allocation when multiple flows compete for limited buffer capacity.

Yuqi Liu

Rui Han

Xu Wang

A Reordering Buffer Management Method at Edge Gateway in Hybrid IP-ICN Multipath Transmission System

混合IP-ICN多路径传输系统中边缘网关的重排序缓冲区管理方法

Gaia-X, a European initiative, aims to create a digital sovereignty framework for service ecosystems in the future Internet. Its applicability to the health domain was explored in the Gaia-X-Med project, which aimed to establish a common dataspace for various medical use cases based on Gaia-X principles. This paper presents a trust- and consent-based approach to the secure authentication and digital contract negotiation central to this endeavor and discusses the challenges that arose during the adoption of the Gaia-X framework, particularly relating to the strict requirements of the European healthcare domain with regards to privacy and consent regulations. By exploring the practical implications of Gaia-X in the healthcare context, this paper aims to contribute to the ongoing discussions surrounding the digital sovereignty of both citizens and corporations, as well as its realization via future Internet technologies.

Bennet Gerlach

Hannes Hesse

Stefan Fischer

Martin Leucker

Gaia-X-Med: Experiences with Building Dataspaces for Medical Applications

Gaia-X-Med: 构建医疗应用数据空间的经验

The rapid development of specific-purpose Large Language Models (LLMs), such as Med-PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering unprecedented capabilities in clinical decision support, diagnostics, and personalized health monitoring. This paper reviews the advancements in medicine-specific LLMs, the integration of Retrieval-Augmented Generation (RAG) and prompt engineering, and their applications in improving diagnostic accuracy and educational utility. Despite the potential, these technologies present challenges, including bias, hallucinations, and the need for robust safety protocols. The paper also discusses the regulatory and ethical considerations necessary for integrating these models into mainstream healthcare. By examining current studies and developments, this paper aims to provide a comprehensive overview of the state of LLMs in medicine and highlight the future directions for research and application. The study concludes that while LLMs hold immense potential, their safe and effective integration into clinical practice requires rigorous testing, ongoing evaluation, and continuous collaboration among stakeholders.

Jiayu Shi

Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’

医学与医疗保健中的生成式人工智能：超越“期望膨胀顶峰”

The acquisition of cybersecurity threat intelligence is a critical task in the implementation of effective security defense strategies. Recently, advancements in large language model (LLM) technology have led to remarkable capabilities in natural language processing and understanding. In this paper, we introduce an LLM-based approach for open-source intelligence (OSINT) acquisition. This approach autonomously obtains OSINT based on user requirements, eliminating the need for manual scanning or querying, thus saving significant time and effort. To further address the knowledge limitations and timeliness challenges inherent in LLMs when handling threat intelligence, we propose a framework that integrates chain-of-thought techniques to assist LLMs in utilizing tools to acquire OSINT. Based on this framework, we have developed a threat intelligence acquisition agent capable of decomposing logical reasoning problems into multiple steps and gradually solving them using appropriate tools, along with a toolkit for the agent to dynamically access during the problem-solving process. To validate the effectiveness of our approach, we have designed four evaluation metrics to assess the agent’s performance and constructed a test set. Experimental results indicate that the agent achieves high accuracy rates in OSINT acquisition tasks, with a substantial improvement noted over its baseline large language model counterpart in specific intelligence acquisition scenarios.

短名	Future. Internet.
Journal Impact	2.72
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q2)

ISSN	1999-5903
h-index	60

涉及主题	计算机科学操作系统工程类数学万维网人工智能计算机安全政治学法学计算机网络程序设计语言生物物理经济哲学电信地理机器学习量子力学心理学统计业务
出版信息	出版商: Multidisciplinary Digital Publishing Institute (MDPI)，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2009，原创研究文献占比: 90.81%，自引率:7.10%， Gold OA占比: 99.17%
期刊费用	$详情
平均审稿周期	网友分享经验：11 Weeks

Future Internet

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