This study investigates the ability of transformer-based models (TBMs) to form stimulus equivalence (SE) classes. We employ BERT and GPT as TBM agents in SE tasks, evaluating their performance across training structures (linear series, one-to-many and many-to-one) and relation types (select–reject, select-only). Our findings demonstrate that both models performed above mastery criterion in the baseline phase across all simulations (n = 12). However, they exhibit limited success in reflexivity, transitivity, and symmetry tests. Notably, both models achieved success only in the linear series structure with select–reject relations, failing in one-to-many and many-to-one structures, and all select-only conditions. These results suggest that TBM may be forming decision rules based on learned discriminations and reject relations, rather than responding according to equivalence class formation. The absence of reject relations appears to influence their responses and the occurrence of hallucinations. This research highlights the potential of SE simulations for: (a) comparative analysis of learning mechanisms, (b) explainability techniques for TBM decision-making, and (c) TBM bench-marking independent of pre-training or fine-tuning. Future investigations can explore upscaling simulations and utilize SE tasks within a reinforcement learning framework.
本研究探讨了基于变换器的模型（TBM）形成刺激等价类（SE）的能力。我们使用 BERT 和 GPT 作为 TBM 代理执行 SE 任务，评估它们在不同训练结构（线性序列、一对多和多对一）和关系类型（选择-拒绝、只选择）下的表现。我们的研究结果表明，在所有模拟（n = 12）中，这两种模型在基线阶段的表现都超过了掌握标准。然而，它们在反身性、反转性和对称性测试中表现出有限的成功。值得注意的是，这两个模型都只在具有选择-拒绝关系的线性序列结构中取得了成功，而在一对多和多对一结构以及所有仅有选择的条件下都失败了。这些结果表明，TBM 可能是根据学习到的判别和拒绝关系形成决策规则，而不是根据等价类的形成做出反应。拒绝关系的缺失似乎会影响他们的反应和幻觉的发生。这项研究凸显了 SE 模拟在以下方面的潜力(a) 学习机制的比较分析，(b) TBM 决策的可解释性技术，(c) 独立于预培训或微调的 TBM 标杆。未来的研究可以探索扩大模拟规模，并在强化学习框架内利用 SE 任务。
{"title":"Testing Stimulus Equivalence in Transformer-Based Agents","authors":"Alexis Carrillo, Moisés Betancort","doi":"10.3390/fi16080289","DOIUrl":"https://doi.org/10.3390/fi16080289","url":null,"abstract":"This study investigates the ability of transformer-based models (TBMs) to form stimulus equivalence (SE) classes. We employ BERT and GPT as TBM agents in SE tasks, evaluating their performance across training structures (linear series, one-to-many and many-to-one) and relation types (select–reject, select-only). Our findings demonstrate that both models performed above mastery criterion in the baseline phase across all simulations (n = 12). However, they exhibit limited success in reflexivity, transitivity, and symmetry tests. Notably, both models achieved success only in the linear series structure with select–reject relations, failing in one-to-many and many-to-one structures, and all select-only conditions. These results suggest that TBM may be forming decision rules based on learned discriminations and reject relations, rather than responding according to equivalence class formation. The absence of reject relations appears to influence their responses and the occurrence of hallucinations. This research highlights the potential of SE simulations for: (a) comparative analysis of learning mechanisms, (b) explainability techniques for TBM decision-making, and (c) TBM bench-marking independent of pre-training or fine-tuning. Future investigations can explore upscaling simulations and utilize SE tasks within a reinforcement learning framework.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing interest in strategies for detecting cross-domain misinformation. However, traditional machine learning (ML) approaches often struggle with the nuanced contextual understanding required for accurate news classification. To address these challenges, we propose a novel contextualized cross-domain prompt-based zero-shot approach utilizing a pre-trained Generative Pre-trained Transformer (GPT) model for fake news detection (FND). In contrast to conventional fine-tuning methods reliant on extensive labeled datasets, our approach places particular emphasis on refining prompt integration and classification logic within the model’s framework. This refinement enhances the model’s ability to accurately classify fake news across diverse domains. Additionally, the adaptability of our approach allows for customization across diverse tasks by modifying prompt placeholders. Our research significantly advances zero-shot learning by demonstrating the efficacy of prompt-based methodologies in text classification, particularly in scenarios with limited training data. Through extensive experimentation, we illustrate that our method effectively captures domain-specific features and generalizes well to other domains, surpassing existing models in terms of performance. These findings contribute significantly to the ongoing efforts to combat fake news dissemination, particularly in environments with severely limited training data, such as online platforms.
{"title":"Cross-Domain Fake News Detection Using a Prompt-Based Approach","authors":"Jawaher Alghamdi, Yuqing Lin, Suhuai Luo","doi":"10.3390/fi16080286","DOIUrl":"https://doi.org/10.3390/fi16080286","url":null,"abstract":"The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing interest in strategies for detecting cross-domain misinformation. However, traditional machine learning (ML) approaches often struggle with the nuanced contextual understanding required for accurate news classification. To address these challenges, we propose a novel contextualized cross-domain prompt-based zero-shot approach utilizing a pre-trained Generative Pre-trained Transformer (GPT) model for fake news detection (FND). In contrast to conventional fine-tuning methods reliant on extensive labeled datasets, our approach places particular emphasis on refining prompt integration and classification logic within the model’s framework. This refinement enhances the model’s ability to accurately classify fake news across diverse domains. Additionally, the adaptability of our approach allows for customization across diverse tasks by modifying prompt placeholders. Our research significantly advances zero-shot learning by demonstrating the efficacy of prompt-based methodologies in text classification, particularly in scenarios with limited training data. Through extensive experimentation, we illustrate that our method effectively captures domain-specific features and generalizes well to other domains, surpassing existing models in terms of performance. These findings contribute significantly to the ongoing efforts to combat fake news dissemination, particularly in environments with severely limited training data, such as online platforms.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressing these platforms. This paper discusses six of the most popular emerging blockchain platforms adopted by IoT systems and analyses their usage in state-of-the-art works to solve security problems. The platform was compared in terms of security features and other requirements. Findings from the study reveal that most blockchain components contribute directly or indirectly to IoT security. Blockchain platform components such as cryptography, consensus mechanism, and hashing are common ways that security is achieved in all blockchain platform for IoT. Technologies like Interplanetary File System (IPFS) and Transport Layer Security (TLS) can further enhance data and communication security when used alongside blockchain. To enhance the applicability of blockchain in resource-constrained IoT environments, future research should focus on refining cryptographic algorithms and consensus mechanisms to optimise performance and security.
{"title":"A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things","authors":"Yunus Kareem, D. Djenouri, Essam Ghadafi","doi":"10.3390/fi16080285","DOIUrl":"https://doi.org/10.3390/fi16080285","url":null,"abstract":"The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressing these platforms. This paper discusses six of the most popular emerging blockchain platforms adopted by IoT systems and analyses their usage in state-of-the-art works to solve security problems. The platform was compared in terms of security features and other requirements. Findings from the study reveal that most blockchain components contribute directly or indirectly to IoT security. Blockchain platform components such as cryptography, consensus mechanism, and hashing are common ways that security is achieved in all blockchain platform for IoT. Technologies like Interplanetary File System (IPFS) and Transport Layer Security (TLS) can further enhance data and communication security when used alongside blockchain. To enhance the applicability of blockchain in resource-constrained IoT environments, future research should focus on refining cryptographic algorithms and consensus mechanisms to optimise performance and security.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online shopping for clothing has become increasingly popular among many people. However, this trend comes with its own set of challenges. For example, it can be difficult for customers to make informed purchase decisions without trying on the clothes to see how they move and flow. We address this issue by introducing a new image-to-video generator called FashionFlow to generate fashion videos to show how clothing products move and flow on a person. By utilising a latent diffusion model and various other components, we are able to synthesise a high-fidelity video conditioned by a fashion image. The components include the use of pseudo-3D convolution, VAE, CLIP, frame interpolator and attention to generate a smooth video efficiently while preserving vital characteristics from the conditioning image. The contribution of our work is the creation of a model that can synthesise videos from images. We show how we use a pre-trained VAE decoder to process the latent space and generate a video. We demonstrate the effectiveness of our local and global conditioners, which help preserve the maximum amount of detail from the conditioning image. Our model is unique because it produces spontaneous and believable motion using only one image, while other diffusion models are either text-to-video or image-to-video using pre-recorded pose sequences. Overall, our research demonstrates a successful synthesis of fashion videos featuring models posing from various angles, showcasing the movement of the garment. Our findings hold great promise for improving and enhancing the online fashion industry’s shopping experience.
{"title":"Dynamic Fashion Video Synthesis from Static Imagery","authors":"Tasin Islam, A. Miron, Xiaohui Liu, Yongmin Li","doi":"10.3390/fi16080287","DOIUrl":"https://doi.org/10.3390/fi16080287","url":null,"abstract":"Online shopping for clothing has become increasingly popular among many people. However, this trend comes with its own set of challenges. For example, it can be difficult for customers to make informed purchase decisions without trying on the clothes to see how they move and flow. We address this issue by introducing a new image-to-video generator called FashionFlow to generate fashion videos to show how clothing products move and flow on a person. By utilising a latent diffusion model and various other components, we are able to synthesise a high-fidelity video conditioned by a fashion image. The components include the use of pseudo-3D convolution, VAE, CLIP, frame interpolator and attention to generate a smooth video efficiently while preserving vital characteristics from the conditioning image. The contribution of our work is the creation of a model that can synthesise videos from images. We show how we use a pre-trained VAE decoder to process the latent space and generate a video. We demonstrate the effectiveness of our local and global conditioners, which help preserve the maximum amount of detail from the conditioning image. Our model is unique because it produces spontaneous and believable motion using only one image, while other diffusion models are either text-to-video or image-to-video using pre-recorded pose sequences. Overall, our research demonstrates a successful synthesis of fashion videos featuring models posing from various angles, showcasing the movement of the garment. Our findings hold great promise for improving and enhancing the online fashion industry’s shopping experience.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Luschi, Giovanni Luca Daino, Gianpaolo Ghisalberti, Vincenzo Mezzatesta, E. Iadanza
The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union’s Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, “Le Scotte” in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital’s existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network.
{"title":"Empowering Clinical Engineering and Evidence-Based Maintenance with IoT and Indoor Navigation","authors":"A. Luschi, Giovanni Luca Daino, Gianpaolo Ghisalberti, Vincenzo Mezzatesta, E. Iadanza","doi":"10.3390/fi16080263","DOIUrl":"https://doi.org/10.3390/fi16080263","url":null,"abstract":"The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union’s Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, “Le Scotte” in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital’s existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Shabbir, Muhammad Faizan Shirazi, Safdar Rizvi, Sadique Ahmad, A. Ateya
This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks.
本研究致力于通过综合方法提高异构网络（HetNets）内的能源效率（EE）。首先，我们通过实施基于随机几何泊松过程分布的双层网络架构，建立了一个基础框架。通过这种部署，我们开发了一个量身定制的 EE 模型，细致分析了随机基站和用户分布对能效的影响。我们制定了基站和用户的联合密度，在满足严格的服务质量（QoS）要求的同时，对 EE 进行了优化。随后，我们引入了一种新颖的动态分布式机会睡眠策略（D-DOSS）来优化 EE。该策略在整个网络中战略性地集群基站，并根据实时流量负载阈值动态调整其睡眠模式。通过使用 MATLAB 进行蒙特卡罗模拟，我们严格评估了 D-DOSS 方法的功效，量化了关键 QoS 参数（如覆盖概率、能量利用效率 (EUE)、成功概率和数据吞吐量）的改善情况。总之，我们的研究向优化 HetNets 中的 EE 迈出了重要一步，同时解决了网络架构优化问题，并提出了一种创新的睡眠管理策略，为未来无线网络的能源效率最大化提供了实用的解决方案。
{"title":"Energy Efficiency and Load Optimization in Heterogeneous Networks through Dynamic Sleep Strategies: A Constraint-Based Optimization Approach","authors":"A. Shabbir, Muhammad Faizan Shirazi, Safdar Rizvi, Sadique Ahmad, A. Ateya","doi":"10.3390/fi16080262","DOIUrl":"https://doi.org/10.3390/fi16080262","url":null,"abstract":"This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In modern network security setups, Intrusion Detection Systems (IDS) are crucial elements that play a key role in protecting against unauthorized access, malicious actions, and policy breaches. Despite significant progress in IDS technology, two of the most major obstacles remain: how to avoid false alarms due to imbalanced data and accurately forecast the precise type of attacks before they even happen to minimize the damage caused. To deal with two problems in the most optimized way possible, we propose a two-task regression and classification strategy called Hybrid Regression–Classification (HRC), a deep learning-based strategy for developing an intrusion detection system (IDS) that can minimize the false alarm rate and detect and predict potential cyber-attacks before they occur to help the current wireless network in dealing with the attacks more efficiently and precisely. The experimental results show that our HRC strategy accurately predicts the incoming behavior of the IP data traffic in two different datasets. This can help the IDS to detect potential attacks sooner with high accuracy so that they can have enough reaction time to deal with the attack. Furthermore, our proposed strategy can also deal with imbalanced data. Even when the imbalance is large between categories. This will help significantly reduce the false alarm rate of IDS in practice. These strengths combined will benefit the IDS by making it more active in defense and help deal with the intrusion detection problem more effectively.
{"title":"A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network","authors":"Khoa Dinh Nguyen Dang, P. Fazio, Miroslav Voznák","doi":"10.3390/fi16080264","DOIUrl":"https://doi.org/10.3390/fi16080264","url":null,"abstract":"In modern network security setups, Intrusion Detection Systems (IDS) are crucial elements that play a key role in protecting against unauthorized access, malicious actions, and policy breaches. Despite significant progress in IDS technology, two of the most major obstacles remain: how to avoid false alarms due to imbalanced data and accurately forecast the precise type of attacks before they even happen to minimize the damage caused. To deal with two problems in the most optimized way possible, we propose a two-task regression and classification strategy called Hybrid Regression–Classification (HRC), a deep learning-based strategy for developing an intrusion detection system (IDS) that can minimize the false alarm rate and detect and predict potential cyber-attacks before they occur to help the current wireless network in dealing with the attacks more efficiently and precisely. The experimental results show that our HRC strategy accurately predicts the incoming behavior of the IP data traffic in two different datasets. This can help the IDS to detect potential attacks sooner with high accuracy so that they can have enough reaction time to deal with the attack. Furthermore, our proposed strategy can also deal with imbalanced data. Even when the imbalance is large between categories. This will help significantly reduce the false alarm rate of IDS in practice. These strengths combined will benefit the IDS by making it more active in defense and help deal with the intrusion detection problem more effectively.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In resource-intensive Internet of Things applications, Lightweight Stream Ciphers (LWSCs) play a vital role in influencing both the security and performance of the system. Numerous LWSCs have been proposed, each offering certain properties and trade-offs that carefully balance security and performance requirements. This paper presents a comprehensive evaluation of prominent LWSCs, with a focus on their performance and resource consumption, providing insights into efficiency, efficacy, and suitability in the real-world application of resource-intensive live video feed encryption on an ARM processor. The study involves the development of a benchmarking tool designed to evaluate key metrics, including encryption frame rate, throughput, processing cycles, memory footprint, ROM utilization, and energy consumption. In addition, we apply the E−Rank metric, which combines key performance and resource metrics to derive a unified comparative measure for overall software performance.
在资源密集型物联网应用中，轻量级流密码（LWSC）在影响系统的安全性和性能方面发挥着至关重要的作用。目前已提出了许多 LWSC，每种 LWSC 都具有一定的特性，并能在安全性和性能要求之间进行权衡。本文全面评估了著名的 LWSC，重点关注其性能和资源消耗，深入探讨了 ARM 处理器上资源密集型实时视频馈送加密实际应用的效率、功效和适用性。这项研究包括开发一个基准测试工具，用于评估关键指标，包括加密帧率、吞吐量、处理周期、内存占用、ROM 利用率和能耗。此外，我们还应用了 E-Rank 指标，该指标将关键性能指标和资源指标结合在一起，为软件的整体性能提供了一个统一的比较衡量标准。
{"title":"Performance Evaluation of Lightweight Stream Ciphers for Real-Time Video Feed Encryption on ARM Processor","authors":"Mohsin Khan, Håvard J Dagenborg, D. Johansen","doi":"10.3390/fi16080261","DOIUrl":"https://doi.org/10.3390/fi16080261","url":null,"abstract":"In resource-intensive Internet of Things applications, Lightweight Stream Ciphers (LWSCs) play a vital role in influencing both the security and performance of the system. Numerous LWSCs have been proposed, each offering certain properties and trade-offs that carefully balance security and performance requirements. This paper presents a comprehensive evaluation of prominent LWSCs, with a focus on their performance and resource consumption, providing insights into efficiency, efficacy, and suitability in the real-world application of resource-intensive live video feed encryption on an ARM processor. The study involves the development of a benchmarking tool designed to evaluate key metrics, including encryption frame rate, throughput, processing cycles, memory footprint, ROM utilization, and energy consumption. In addition, we apply the E−Rank metric, which combines key performance and resource metrics to derive a unified comparative measure for overall software performance.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems.
{"title":"Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems","authors":"Chuanzhe Gao, Shidang Li, Yixuan Wu, Siyi Duan, Mingsheng Wei, Bencheng Yu","doi":"10.3390/fi16080266","DOIUrl":"https://doi.org/10.3390/fi16080266","url":null,"abstract":"Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bufferbloat is one of the leading causes of high data transmission latency and jitter on the Internet, which severely impacts the performance of low-latency interactive applications such as online streaming, cloud-based gaming/applications, Internet of Things (IoT) applications, voice over IP (VoIP), real-time video conferencing, and so forth. There is currently a pressing need for developing Transmission Control Protocol (TCP) congestion control algorithms and bottleneck queue management schemes that can collaboratively control/reduce end-to-end latency, thus ensuring optimal quality of service (QoS) and quality of experience (QoE) for users. This paper introduces a novel solution by experimentally integrate the low latency, low loss, and scalable throughput (L4S) architecture (specified by the IETF in RFC 9330) in FreeBSD framework with the asynchronous advantage actor-critic (A3C) reinforcement learning algorithm. The first phase involves incorporating a modified dual-queue coupled active queue management (AQM) system for L4S into the FreeBSD networking stack, enhancing queue management and mitigating latency and packet loss. The second phase employs A3C to adjust and fine-tune the system performance dynamically. Finally, we evaluate the proposed solution’s effectiveness through comprehensive experiments, comparing it with traditional AQM-based systems. This paper contributes to the advancement of machine learning (ML) for transport protocol research in the field. The experimental implementation and results presented in this paper are made available through our GitHub repositories.
{"title":"Active Queue Management in L4S with Asynchronous Advantage Actor-Critic: A FreeBSD Networking Stack Perspective","authors":"Deol Satish, Jonathan Kua, Shiva Raj Pokhrel","doi":"10.3390/fi16080265","DOIUrl":"https://doi.org/10.3390/fi16080265","url":null,"abstract":"Bufferbloat is one of the leading causes of high data transmission latency and jitter on the Internet, which severely impacts the performance of low-latency interactive applications such as online streaming, cloud-based gaming/applications, Internet of Things (IoT) applications, voice over IP (VoIP), real-time video conferencing, and so forth. There is currently a pressing need for developing Transmission Control Protocol (TCP) congestion control algorithms and bottleneck queue management schemes that can collaboratively control/reduce end-to-end latency, thus ensuring optimal quality of service (QoS) and quality of experience (QoE) for users. This paper introduces a novel solution by experimentally integrate the low latency, low loss, and scalable throughput (L4S) architecture (specified by the IETF in RFC 9330) in FreeBSD framework with the asynchronous advantage actor-critic (A3C) reinforcement learning algorithm. The first phase involves incorporating a modified dual-queue coupled active queue management (AQM) system for L4S into the FreeBSD networking stack, enhancing queue management and mitigating latency and packet loss. The second phase employs A3C to adjust and fine-tune the system performance dynamically. Finally, we evaluate the proposed solution’s effectiveness through comprehensive experiments, comparing it with traditional AQM-based systems. This paper contributes to the advancement of machine learning (ML) for transport protocol research in the field. The experimental implementation and results presented in this paper are made available through our GitHub repositories.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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