Shiva Raj Pokhrel, Jonathan Kua, Deol Satish, Sebnem Ozer, Jeff Howe, Anwar Walid
We introduce a novel multipath data transport approach at the transport layer referred to as ‘Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control’ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.
{"title":"DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control","authors":"Shiva Raj Pokhrel, Jonathan Kua, Deol Satish, Sebnem Ozer, Jeff Howe, Anwar Walid","doi":"10.3390/fi16020037","DOIUrl":"https://doi.org/10.3390/fi16020037","url":null,"abstract":"We introduce a novel multipath data transport approach at the transport layer referred to as ‘Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control’ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499227","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}
Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, N. Kaabouch
With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.
随着机器学习（ML）在各个应用领域的快速发展和显著成就，它已成为物联网（IoT）生态系统中的一个重要元素。这些用例中包括物联网安全，其中部署了许多系统来识别或挫败攻击，包括入侵检测系统 (IDS)、恶意软件检测系统 (MDS) 和设备识别系统 (DIS)。基于机器学习（ML）的物联网安全系统可以实现多个安全目标，包括检测攻击、在用户访问系统前对其进行身份验证以及对可疑活动进行分类。然而，ML 也面临着许多挑战，例如，为误导分类器而精心设计的对抗性攻击的出现。本文全面回顾了有关对抗性攻击和防御机制的知识体系，并特别关注三个著名的物联网安全系统：IDS、MDS 和 DIS。本文首先对物联网背景下的对抗性攻击进行了分类。然后，在一个二维框架内描述了生成对抗性攻击所采用的各种方法，并对其进行了分类。此外，我们还介绍了增强物联网安全性、抵御对抗性攻击的现有对策。最后，我们探讨了三种基于 ML 的物联网安全系统在对抗性攻击面前的脆弱性的最新文献。
{"title":"A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks","authors":"Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, N. Kaabouch","doi":"10.3390/fi16010032","DOIUrl":"https://doi.org/10.3390/fi16010032","url":null,"abstract":"With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524890","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}
Zhengyang Fan, Wanru Li, Kathryn B. Laskey, Kuo-Chu Chang
Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.
{"title":"Investigation of Phishing Susceptibility with Explainable Artificial Intelligence","authors":"Zhengyang Fan, Wanru Li, Kathryn B. Laskey, Kuo-Chu Chang","doi":"10.3390/fi16010031","DOIUrl":"https://doi.org/10.3390/fi16010031","url":null,"abstract":"Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527826","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 detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrusion detection. However, their enhancement root causes usually present weak relationships to the differences between normal and attack behaviors in the data samples. Thus, we propose a Classification Tendency Difference Index (CTDI) model for feature selection and extraction in intrusion detection. The CTDI model consists of three indexes: Classification Tendency Frequency Difference (CTFD), Classification Tendency Membership Difference (CTMD), and Classification Tendency Distance Difference (CTDD). In the dataset, each feature has many feature values (FVs). In each FV, the normal and attack samples indicate the FV classification tendency, and CTDI shows the classification tendency differences between the normal and attack samples. CTFD is the frequency difference between the normal and attack samples. By employing fuzzy C means (FCM) to establish the normal and attack clusters, CTMD is the membership difference between the clusters, and CTDD is the distance difference between the cluster centers. CTDI calculates the index score in each FV and summarizes the scores of all FVs in the feature as the feature score for each of the three indexes. CTDI adopts an Auto Encoder for feature extraction to generate new features from the dataset and calculate the three index scores for the new features. CTDI sorts the original and new features for each of the three indexes to select the best features. The selected CTDI features indicate the best classification tendency differences between normal and attack samples. The experiment results demonstrate that the CTDI features achieve better detection accuracy as classified by DNN for the Aegean WiFi Intrusion Dataset than their related works, and the detection enhancements are based on the improved classification tendency differences in the CTDI features.
{"title":"Classification Tendency Difference Index Model for Feature Selection and Extraction in Wireless Intrusion Detection","authors":"C. Tseng, Woei-Jiunn Tsaur, Yueh-Mao Shen","doi":"10.3390/fi16010025","DOIUrl":"https://doi.org/10.3390/fi16010025","url":null,"abstract":"In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrusion detection. However, their enhancement root causes usually present weak relationships to the differences between normal and attack behaviors in the data samples. Thus, we propose a Classification Tendency Difference Index (CTDI) model for feature selection and extraction in intrusion detection. The CTDI model consists of three indexes: Classification Tendency Frequency Difference (CTFD), Classification Tendency Membership Difference (CTMD), and Classification Tendency Distance Difference (CTDD). In the dataset, each feature has many feature values (FVs). In each FV, the normal and attack samples indicate the FV classification tendency, and CTDI shows the classification tendency differences between the normal and attack samples. CTFD is the frequency difference between the normal and attack samples. By employing fuzzy C means (FCM) to establish the normal and attack clusters, CTMD is the membership difference between the clusters, and CTDD is the distance difference between the cluster centers. CTDI calculates the index score in each FV and summarizes the scores of all FVs in the feature as the feature score for each of the three indexes. CTDI adopts an Auto Encoder for feature extraction to generate new features from the dataset and calculate the three index scores for the new features. CTDI sorts the original and new features for each of the three indexes to select the best features. The selected CTDI features indicate the best classification tendency differences between normal and attack samples. The experiment results demonstrate that the CTDI features achieve better detection accuracy as classified by DNN for the Aegean WiFi Intrusion Dataset than their related works, and the detection enhancements are based on the improved classification tendency differences in the CTDI features.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533000","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}
This paper presents a comprehensive set of design methods for making future Internet networking fully energy-aware and sustainably minimizing and managing the energy footprint. It includes (a) 41 energy-aware design methods, grouped into Service Operations Support, Management Operations Support, Compute Operations Support, Connectivity/Forwarding Operations Support, Traffic Engineering Methods, Architectural Support for Energy Instrumentation, and Network Configuration; (b) energy consumption models and energy metrics are identified and specified. It specifies the requirements for energy-defined network compliance, which include energy-measurable network devices with the support of several control messages: registration, discovery, provisioning, discharge, monitoring, synchronization, flooding, performance, and pushback.
{"title":"Future Sustainable Internet Energy-Defined Networking","authors":"Alex Galis","doi":"10.3390/fi16010023","DOIUrl":"https://doi.org/10.3390/fi16010023","url":null,"abstract":"This paper presents a comprehensive set of design methods for making future Internet networking fully energy-aware and sustainably minimizing and managing the energy footprint. It includes (a) 41 energy-aware design methods, grouped into Service Operations Support, Management Operations Support, Compute Operations Support, Connectivity/Forwarding Operations Support, Traffic Engineering Methods, Architectural Support for Energy Instrumentation, and Network Configuration; (b) energy consumption models and energy metrics are identified and specified. It specifies the requirements for energy-defined network compliance, which include energy-measurable network devices with the support of several control messages: registration, discovery, provisioning, discharge, monitoring, synchronization, flooding, performance, and pushback.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442471","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 the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency.
{"title":"A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness","authors":"M. Elkhodr, Samiya Khan, E. Gide","doi":"10.3390/fi16010022","DOIUrl":"https://doi.org/10.3390/fi16010022","url":null,"abstract":"In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448816","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}
Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.
{"title":"Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT","authors":"Lidong Liu, Shidang Li, Mingsheng Wei, Jinsong Xu, Bencheng Yu","doi":"10.3390/fi16010020","DOIUrl":"https://doi.org/10.3390/fi16010020","url":null,"abstract":"Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449443","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}
G. M. N. Ali, Mohammad Nazmus Sadat, Md. Suruz Miah, Sameer Ahmed Sharief, Yun Wang
Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.
最近，第三代合作伙伴计划（3GPP）为车对万物（V2X）通信引入了新的无线电（NR）技术，以支持车辆通信中对延迟敏感和带宽要求高的应用。NR 系统是对现有的长期演进（LTE）蜂窝-车载-万物（C-V2X）基础设施的战略性补充，特别是支持自动驾驶汽车运行等先进服务。人们普遍预计，第五代（5G）NR 系统将超越 LTE C-V2X，在以高吞吐量、低延迟和增强可靠性为特征的场景中实现卓越性能，特别是在交通拥堵的条件下和各种车辆应用中。本文将对从专用短程通信（DSRC）到 NR V2X 的车辆通信进行全面的文献综述。随后，文章将详细探讨 NR V2X 技术所固有的挑战和机遇。最后，我们将阐明在网络仿真-3（ns-3）中创建和分析开源 5G NR V2X 模块的过程，然后通过不同的运行场景，从不同的关键性能指标方面展示 NR V2X 的性能。
{"title":"A Comprehensive Study and Analysis of the Third Generation Partnership Project’s 5G New Radio for Vehicle-to-Everything Communication","authors":"G. M. N. Ali, Mohammad Nazmus Sadat, Md. Suruz Miah, Sameer Ahmed Sharief, Yun Wang","doi":"10.3390/fi16010021","DOIUrl":"https://doi.org/10.3390/fi16010021","url":null,"abstract":"Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449355","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}
Chen Zhang, Celimuge Wu, Min Lin, Yangfei Lin, William Liu
In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.
{"title":"Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing","authors":"Chen Zhang, Celimuge Wu, Min Lin, Yangfei Lin, William Liu","doi":"10.3390/fi16010019","DOIUrl":"https://doi.org/10.3390/fi16010019","url":null,"abstract":"In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390866","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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