Pub Date : 2024-09-05DOI: 10.1109/TNSM.2024.3455105
Hansini Vijayaraghavan;Wolfgang Kellerer
This paper presents MobiFi, a framework addressing the challenges in managing LiFi-WiFi heterogeneous networks focusing on mobility-aware resource allocation. Our contributions include introducing a centralized framework incorporating reactive and proactive strategies for resource management in mobile LiFi-only and LiFi-WiFi networks. This framework reacts to current network conditions and proactively anticipates the future, considering user positions, line-of-sight blockages, and channel quality. Recognizing the importance of long-term network performance, particularly for use cases such as video streaming, we tackle the challenge of optimal proactive resource allocation by formulating an optimization problem that integrates access point assignment and wireless resource allocation using the alpha-fairness objective over time. Our proactive strategy significantly outperforms the reactive resource allocation, ensuring 7.7% higher average rate and 63.3% higher minimum user rate for a 10-user LiFi-WiFi network. We employ sophisticated techniques, including a Branch and Bound-based Mixed-Integer solver and a low-complexity, Evolutionary Game Theory-based algorithm to achieve this. Lastly, we introduce a novel approach to simulate errors in predictive user position modeling to assess the robustness of our proactive allocation strategy against real-world uncertainties. The contributions of MobiFi advance the field of resource management in mobile LiFi-WiFi networks, enabling efficiency and reliability.
{"title":"MobiFi: Mobility-Aware Reactive and Proactive Wireless Resource Management in LiFi-WiFi Networks","authors":"Hansini Vijayaraghavan;Wolfgang Kellerer","doi":"10.1109/TNSM.2024.3455105","DOIUrl":"10.1109/TNSM.2024.3455105","url":null,"abstract":"This paper presents MobiFi, a framework addressing the challenges in managing LiFi-WiFi heterogeneous networks focusing on mobility-aware resource allocation. Our contributions include introducing a centralized framework incorporating reactive and proactive strategies for resource management in mobile LiFi-only and LiFi-WiFi networks. This framework reacts to current network conditions and proactively anticipates the future, considering user positions, line-of-sight blockages, and channel quality. Recognizing the importance of long-term network performance, particularly for use cases such as video streaming, we tackle the challenge of optimal proactive resource allocation by formulating an optimization problem that integrates access point assignment and wireless resource allocation using the alpha-fairness objective over time. Our proactive strategy significantly outperforms the reactive resource allocation, ensuring 7.7% higher average rate and 63.3% higher minimum user rate for a 10-user LiFi-WiFi network. We employ sophisticated techniques, including a Branch and Bound-based Mixed-Integer solver and a low-complexity, Evolutionary Game Theory-based algorithm to achieve this. Lastly, we introduce a novel approach to simulate errors in predictive user position modeling to assess the robustness of our proactive allocation strategy against real-world uncertainties. The contributions of MobiFi advance the field of resource management in mobile LiFi-WiFi networks, enabling efficiency and reliability.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6597-6613"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/TNSM.2024.3454758
Binbin Lu;Yuan Wu;Liping Qian;Sheng Zhou;Haixia Zhang;Rongxing Lu
Network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the Quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.
{"title":"Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications","authors":"Binbin Lu;Yuan Wu;Liping Qian;Sheng Zhou;Haixia Zhang;Rongxing Lu","doi":"10.1109/TNSM.2024.3454758","DOIUrl":"10.1109/TNSM.2024.3454758","url":null,"abstract":"Network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the Quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6744-6758"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/TNSM.2024.3454777
Yufei An;F. Richard Yu;Ying He;Jianqiang Li;Jianyong Chen;Victor C. M. Leung
The advancement of Internet of Things (IoT) technology has significantly transformed the dynamic between humans and devices, as well as device-to-device interactions. This paradigm shift has led to profound changes in human lifestyles and production processes. Through the interconnectedness of numerous sensors and controllers via networks, the IoT facilitates the seamless integration of humans with diverse devices, leading to substantial economic advantages. Nevertheless, the burgeoning IoT industry and the rapid proliferation of various IoT devices have also introduced a multitude of security vulnerabilities. Cyber attackers frequently exploit cyber attacks to compromise IoT devices, jeopardizing user privacy and property security, thereby posing a grave menace to the overall security of the IoT ecosystem. In this paper, we propose a novel IoT Web attack detection system based on a joint embedded prediction architecture (JEPA), which effectively alleviates the security issues faced by IoT. It can obtain high-level semantic features in IoT traffic data through non-generative self-supervised learning. These features can more effectively distinguish normal data from attack data and help improve the overall detection performance of the system. Moreover, we propose a feature interaction module based on a dual-branch network, which effectively fuses low-level features and high-level features, and comprehensively aggregates global features and local features. Simulation results on multiple datasets show that our proposed system has better detection performance and robustness.
{"title":"A Deep Learning System for Detecting IoT Web Attacks With a Joint Embedded Prediction Architecture (JEPA)","authors":"Yufei An;F. Richard Yu;Ying He;Jianqiang Li;Jianyong Chen;Victor C. M. Leung","doi":"10.1109/TNSM.2024.3454777","DOIUrl":"10.1109/TNSM.2024.3454777","url":null,"abstract":"The advancement of Internet of Things (IoT) technology has significantly transformed the dynamic between humans and devices, as well as device-to-device interactions. This paradigm shift has led to profound changes in human lifestyles and production processes. Through the interconnectedness of numerous sensors and controllers via networks, the IoT facilitates the seamless integration of humans with diverse devices, leading to substantial economic advantages. Nevertheless, the burgeoning IoT industry and the rapid proliferation of various IoT devices have also introduced a multitude of security vulnerabilities. Cyber attackers frequently exploit cyber attacks to compromise IoT devices, jeopardizing user privacy and property security, thereby posing a grave menace to the overall security of the IoT ecosystem. In this paper, we propose a novel IoT Web attack detection system based on a joint embedded prediction architecture (JEPA), which effectively alleviates the security issues faced by IoT. It can obtain high-level semantic features in IoT traffic data through non-generative self-supervised learning. These features can more effectively distinguish normal data from attack data and help improve the overall detection performance of the system. Moreover, we propose a feature interaction module based on a dual-branch network, which effectively fuses low-level features and high-level features, and comprehensively aggregates global features and local features. Simulation results on multiple datasets show that our proposed system has better detection performance and robustness.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6885-6898"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in distributed TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal in a hybrid SDN scenario, we construct a reasonable virtual environment to meet different routing constraints brought by legacy routers and SDN switches for training the routing agents. To train the routing agents for determining the local routing policies according to local network observations, we introduce the difference reward assignment mechanism for encouraging agents to cooperatively take optimal routing action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.
{"title":"Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework","authors":"Yingya Guo;Bin Lin;Qi Tang;Yulong Ma;Huan Luo;Han Tian;Kai Chen","doi":"10.1109/TNSM.2024.3454282","DOIUrl":"10.1109/TNSM.2024.3454282","url":null,"abstract":"Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in distributed TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal in a hybrid SDN scenario, we construct a reasonable virtual environment to meet different routing constraints brought by legacy routers and SDN switches for training the routing agents. To train the routing agents for determining the local routing policies according to local network observations, we introduce the difference reward assignment mechanism for encouraging agents to cooperatively take optimal routing action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6759-6769"},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.
{"title":"Multi-Agent DRL-Based Energy Harvesting for Freshness of Data in UAV-Assisted Wireless Sensor Networks","authors":"Mesfin Leranso Betalo;Supeng Leng;Hayla Nahom Abishu;Abegaz Mohammed Seid;Maged Fakirah;Aiman Erbad;Mohsen Guizani","doi":"10.1109/TNSM.2024.3454217","DOIUrl":"10.1109/TNSM.2024.3454217","url":null,"abstract":"In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6527-6541"},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Software-Defined Mobile Network (SDMN), Multi-Access Edge Computing (MEC), Cloud RAN (C-RAN), and Network Slicing are the promising solutions that have been defined for the next generation of the wireless mobile networks in order to fulfill the increasing Quality of Experience (QoE) demand of the mobile users and the Quality of Service (QoS) concerns of high-performance, innovative services. In today’s complex telecommunications network, coupled with continuous traffic growth, and users’ demand for higher speeds, it is vital for mobile operators to allocate their available resources efficiently. This paper focuses on the joint resource allocation problem of delivering adaptive video streams to users located in different slices of a wireless network enabled by MEC, SDMN, and C-RAN technologies. It proposes a novel Cross-Layer QoE-Driven Bitrate Allocation (CLQDBA) algorithm, that aims to improve system utilization by using information from the higher layers regarding traffic patterns and desired video quality of HTTP Adaptive Streaming (HAS) users. The mixed-integer nonlinear program is formulated, taking into account network slice requirements, radio resource limitations, storage and transcoding capacity of MEC servers, and users’ quality of experience. CLQDBA is a low complexity greedy-based algorithm aims to maximize users’ quality of experience (QoE) and minimize the deviation between the achievable throughput at the MAC-layer for users and the value of allocated bit rates for video frames at the application layer. The simulation result shows that compared to the baseline scheme, our introduced algorithm, on average, achieves a 15% higher system utilization, 17% higher video quality, and 13% improvement of Jain’s Fairness index for HAS users.
{"title":"QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming","authors":"Yashar Farzaneh Yeznabad;Markus Helfert;Gabriel-Miro Muntean","doi":"10.1109/TNSM.2024.3453992","DOIUrl":"10.1109/TNSM.2024.3453992","url":null,"abstract":"The Software-Defined Mobile Network (SDMN), Multi-Access Edge Computing (MEC), Cloud RAN (C-RAN), and Network Slicing are the promising solutions that have been defined for the next generation of the wireless mobile networks in order to fulfill the increasing Quality of Experience (QoE) demand of the mobile users and the Quality of Service (QoS) concerns of high-performance, innovative services. In today’s complex telecommunications network, coupled with continuous traffic growth, and users’ demand for higher speeds, it is vital for mobile operators to allocate their available resources efficiently. This paper focuses on the joint resource allocation problem of delivering adaptive video streams to users located in different slices of a wireless network enabled by MEC, SDMN, and C-RAN technologies. It proposes a novel Cross-Layer QoE-Driven Bitrate Allocation (CLQDBA) algorithm, that aims to improve system utilization by using information from the higher layers regarding traffic patterns and desired video quality of HTTP Adaptive Streaming (HAS) users. The mixed-integer nonlinear program is formulated, taking into account network slice requirements, radio resource limitations, storage and transcoding capacity of MEC servers, and users’ quality of experience. CLQDBA is a low complexity greedy-based algorithm aims to maximize users’ quality of experience (QoE) and minimize the deviation between the achievable throughput at the MAC-layer for users and the value of allocated bit rates for video frames at the application layer. The simulation result shows that compared to the baseline scheme, our introduced algorithm, on average, achieves a 15% higher system utilization, 17% higher video quality, and 13% improvement of Jain’s Fairness index for HAS users.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6857-6874"},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1109/TNSM.2024.3453917
Yunlong Deng;Tao Peng;Bangchao Wang;Gan Wu
The escalating growth of network technology and users poses critical challenges to network security. This paper introduces ANDE, a novel framework designed to enhance the classification accuracy of anonymity networks. ANDE incorporates both raw data features and statistical features extracted from network traffic. Raw data features are transformed into images, enabling recognition and classification using robust image domain models. ANDE combines an enhanced Squeeze-and-Excitation (SE) ResNet with Multilayer Perceptrons (MLP), facilitating concurrent learning and classification of both feature types. Extensive experiments on two publicly available datasets demonstrate the superior performance of ANDE compared to traditional machine learning and deep learning methods. The comprehensive evaluation underscores ANDE’s effectiveness in accurately classifying network traffic within anonymity networks. Additionally, this study empirically validates the efficacy of the SE block in augmenting the classification capabilities of the proposed framework, establishing ANDE as a promising solution for network traffic classification in the realm of network security.
{"title":"ANDE: Detect the Anonymity Web Traffic With Comprehensive Model","authors":"Yunlong Deng;Tao Peng;Bangchao Wang;Gan Wu","doi":"10.1109/TNSM.2024.3453917","DOIUrl":"10.1109/TNSM.2024.3453917","url":null,"abstract":"The escalating growth of network technology and users poses critical challenges to network security. This paper introduces ANDE, a novel framework designed to enhance the classification accuracy of anonymity networks. ANDE incorporates both raw data features and statistical features extracted from network traffic. Raw data features are transformed into images, enabling recognition and classification using robust image domain models. ANDE combines an enhanced Squeeze-and-Excitation (SE) ResNet with Multilayer Perceptrons (MLP), facilitating concurrent learning and classification of both feature types. Extensive experiments on two publicly available datasets demonstrate the superior performance of ANDE compared to traditional machine learning and deep learning methods. The comprehensive evaluation underscores ANDE’s effectiveness in accurately classifying network traffic within anonymity networks. Additionally, this study empirically validates the efficacy of the SE block in augmenting the classification capabilities of the proposed framework, establishing ANDE as a promising solution for network traffic classification in the realm of network security.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6924-6936"},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.
{"title":"Node-Oriented Slice Reconfiguration Based on Spatial and Temporal Traffic Prediction in Metro Optical Networks","authors":"Bowen Bao;Hui Yang;Qiuyan Yao;Jie Zhang;Bijoy Chand Chatterjee;Eiji Oki","doi":"10.1109/TNSM.2024.3453381","DOIUrl":"10.1109/TNSM.2024.3453381","url":null,"abstract":"Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6731-6743"},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, drones have attracted considerable attention for sensing hostile areas. Multiple drones are deployed to communicate and coordinate sensing and data transfer in the Internet of Drones (IoD) network. Traditionally, multi-hop routing is employed for communication over long distances to increase the network’s lifetime. However, multi-hop routing over large-scale networks leads to energy imbalance and higher data latency. Motivated by this, in this paper, a novel framework of energy-efficient and latency-aware data routing is proposed for Small-World (SW)-IoD networks. We started with an optimization problem formulation in terms of network delay, energy consumption, and reliability. Then, the formulated mixed integer problem is solved by introducing the Small-World Characters (SWC) into the conventional IoD network to form the SW-IoD network. Here, the proposed framework introduces SWC by removing a few existing edges with the least edge weight from the traditional network and introducing the same number of long-range edges with the highest edge weight. We present the simulation results corresponding to packet delivery ratio, network lifetime, and network delay for the performance comparison of the proposed framework with state-of-the-art approaches such as the conventional SWC method, LEACH, Modified LEACH, Canonical Particle Multi-Swarm (PMS) method, and conventional shortest path routing algorithm. We also analyze the effect of the location of the ground control station, the velocity of the drones, and the different heights of layers on the performance of the proposed framework. Through experiments, the superiority of the proposed method is proven to be better when compared to other methods. Finally, the performance evaluation of the proposed model is tested on a network simulator (NS3).
最近，无人机因探测敌对地区而备受关注。在无人机互联网（Internet of drones, IoD）网络中，部署多架无人机进行通信和协调传感和数据传输。传统上，多跳路由用于长距离通信，以增加网络的生命周期。但是，在大规模网络中，多跳路由会导致能量不平衡和数据延迟。基于此，本文提出了一种适用于小世界(SW)-IoD网络的节能和延迟感知数据路由的新框架。我们从网络延迟、能耗和可靠性方面的优化问题开始。然后，将小世界字符（Small-World Characters， SWC）引入到传统的IoD网络中，形成SW-IoD网络，解决了公式化的混合整数问题。在这里，提出的框架通过从传统网络中去除一些边缘权值最小的现有边缘，并引入相同数量的边缘权值最高的远程边缘来引入SWC。我们给出了相应的数据包传送率、网络寿命和网络延迟的仿真结果，以将所提出的框架与最先进的方法（如传统的SWC方法、LEACH、改进的LEACH、规范粒子多群（PMS）方法和传统的最短路径路由算法）进行性能比较。我们还分析了地面控制站的位置、无人机的速度和不同层的高度对所提出框架性能的影响。通过实验证明，与其他方法相比，该方法具有更好的优越性。最后，在网络模拟器（NS3）上对该模型进行了性能评估。
{"title":"Energy-Efficient and Latency-Aware Data Routing in Small-World Internet of Drone Networks","authors":"Sreenivasa Reddy Yeduri;Sindhusha Jeeru;Om Jee Pandey;Linga Reddy Cenkeramaddi","doi":"10.1109/TNSM.2024.3452414","DOIUrl":"10.1109/TNSM.2024.3452414","url":null,"abstract":"Recently, drones have attracted considerable attention for sensing hostile areas. Multiple drones are deployed to communicate and coordinate sensing and data transfer in the Internet of Drones (IoD) network. Traditionally, multi-hop routing is employed for communication over long distances to increase the network’s lifetime. However, multi-hop routing over large-scale networks leads to energy imbalance and higher data latency. Motivated by this, in this paper, a novel framework of energy-efficient and latency-aware data routing is proposed for Small-World (SW)-IoD networks. We started with an optimization problem formulation in terms of network delay, energy consumption, and reliability. Then, the formulated mixed integer problem is solved by introducing the Small-World Characters (SWC) into the conventional IoD network to form the SW-IoD network. Here, the proposed framework introduces SWC by removing a few existing edges with the least edge weight from the traditional network and introducing the same number of long-range edges with the highest edge weight. We present the simulation results corresponding to packet delivery ratio, network lifetime, and network delay for the performance comparison of the proposed framework with state-of-the-art approaches such as the conventional SWC method, LEACH, Modified LEACH, Canonical Particle Multi-Swarm (PMS) method, and conventional shortest path routing algorithm. We also analyze the effect of the location of the ground control station, the velocity of the drones, and the different heights of layers on the performance of the proposed framework. Through experiments, the superiority of the proposed method is proven to be better when compared to other methods. Finally, the performance evaluation of the proposed model is tested on a network simulator (NS3).","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6555-6565"},"PeriodicalIF":4.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1109/TNSM.2024.3450993
Yirui Wu;Hao Cao;Yong Lai;Liang Zhao;Xiaoheng Deng;Shaohua Wan
Digital twins (DT) and mobile networks have evolved forms of intelligence in Internet of Things (IoT). In this work, we consider a Digital Twin Mobile Network (DTMN) scenario with few multimedia samples. Facing challenges of knowledge extraction with few samples, stable interaction with dynamic changes of multimedia data, time and privacy saving in low-resource mobile network, we propose an edge computing and few-shot learning featured intelligent framework. Considering time-sensitive property of transmission and privacy risks of directly uploads in mobile network, we deploy edge computing to locally run networks for analysis, thus saving time to offload computing request and enhancing privacy by encrypting original data. Inspired by remarkable relationship representation of graphs, we build Graph Neural Network (GNN) in cloud to map physical mobile systems to virtual entities with DT, thus performing semantic inferences in cloud with few samples uploaded by edges. Occasionally, node features in GNN could converge to similar, non-discriminative embeddings, causing catastrophic unstable phenomena. An iterative reweight and drop structure (IRDS) is thus constructed in cloud, which nonetheless contributes stability with respect to edge uncertainty. As part of IRDS, a drop Edge&Node scheme is proposed to randomly remove certain nodes and edges, which not only enhances distinguished capability of graph neighbor patterns, but also offers data encryption with random strategy. We show one implementation case of image classification in social network, where experiments on public datasets show that our framework is effective with user-friendly advantages and significant intelligence.
{"title":"Edge Computing and Few-Shot Learning Featured Intelligent Framework in Digital Twin Empowered Mobile Networks","authors":"Yirui Wu;Hao Cao;Yong Lai;Liang Zhao;Xiaoheng Deng;Shaohua Wan","doi":"10.1109/TNSM.2024.3450993","DOIUrl":"10.1109/TNSM.2024.3450993","url":null,"abstract":"Digital twins (DT) and mobile networks have evolved forms of intelligence in Internet of Things (IoT). In this work, we consider a Digital Twin Mobile Network (DTMN) scenario with few multimedia samples. Facing challenges of knowledge extraction with few samples, stable interaction with dynamic changes of multimedia data, time and privacy saving in low-resource mobile network, we propose an edge computing and few-shot learning featured intelligent framework. Considering time-sensitive property of transmission and privacy risks of directly uploads in mobile network, we deploy edge computing to locally run networks for analysis, thus saving time to offload computing request and enhancing privacy by encrypting original data. Inspired by remarkable relationship representation of graphs, we build Graph Neural Network (GNN) in cloud to map physical mobile systems to virtual entities with DT, thus performing semantic inferences in cloud with few samples uploaded by edges. Occasionally, node features in GNN could converge to similar, non-discriminative embeddings, causing catastrophic unstable phenomena. An iterative reweight and drop structure (IRDS) is thus constructed in cloud, which nonetheless contributes stability with respect to edge uncertainty. As part of IRDS, a drop Edge&Node scheme is proposed to randomly remove certain nodes and edges, which not only enhances distinguished capability of graph neighbor patterns, but also offers data encryption with random strategy. We show one implementation case of image classification in social network, where experiments on public datasets show that our framework is effective with user-friendly advantages and significant intelligence.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6505-6514"},"PeriodicalIF":4.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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