Pub Date : 2025-09-11DOI: 10.1109/TNSM.2025.3608077
Mounir Bensalem;Admela Jukan
We consider the problem of signaling rate and performance for control and management of reconfigurable intelligent surfaces (RISs) in next-generation mobile networks. To this end, we first analytically determine the rates of RIS reconfigurations and handover using a stochastic geometry network model. We derive closed-form expressions of these rates, while taking into account static obstacles (both known and unknown), self-blockage, RIS location density, and variations in the angle and direction of user mobility. Based on the derived rates, we analyze the signaling rates of a sample novel signaling protocol, which we propose as an extension of the current handover signaling protocol. We evaluate the signaling overhead due to RIS reconfigurations and the related energy consumption. We also provide a capacity planning analysis of the related RIS control plane server for its dimensioning in the network management system. The results quantify the impact of known and unknown obstacles on the RIS reconfiguration rate and the handover rate as a function of device density and mobility. We evaluate the scalability of the model, the related signaling overhead, energy efficiency, and server capacity in the control plane. To the best of our knowledge, this is the first analytical model to derive the closed form expressions of RIS reconfiguration rates, along with handover rates, and relate its statistical properties to the signaling rate and performance in next-generation mobile networks.
{"title":"Signaling Rate and Performance of RIS Reconfiguration and Handover Management in Next Generation Mobile Networks","authors":"Mounir Bensalem;Admela Jukan","doi":"10.1109/TNSM.2025.3608077","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3608077","url":null,"abstract":"We consider the problem of signaling rate and performance for control and management of reconfigurable intelligent surfaces (RISs) in next-generation mobile networks. To this end, we first analytically determine the rates of RIS reconfigurations and handover using a stochastic geometry network model. We derive closed-form expressions of these rates, while taking into account static obstacles (both known and unknown), self-blockage, RIS location density, and variations in the angle and direction of user mobility. Based on the derived rates, we analyze the signaling rates of a sample novel signaling protocol, which we propose as an extension of the current handover signaling protocol. We evaluate the signaling overhead due to RIS reconfigurations and the related energy consumption. We also provide a capacity planning analysis of the related RIS control plane server for its dimensioning in the network management system. The results quantify the impact of known and unknown obstacles on the RIS reconfiguration rate and the handover rate as a function of device density and mobility. We evaluate the scalability of the model, the related signaling overhead, energy efficiency, and server capacity in the control plane. To the best of our knowledge, this is the first analytical model to derive the closed form expressions of RIS reconfiguration rates, along with handover rates, and relate its statistical properties to the signaling rate and performance in next-generation mobile networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 6","pages":"6159-6176"},"PeriodicalIF":5.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11160619","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665795","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 : 2025-09-11DOI: 10.1109/TNSM.2025.3608796
Anna Karanika;Rui Yang;Xiaojuan Ma;Jiangran Wang;Shalni Sundram;Indranil Gupta
While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications with commonplace sense-trigger-actuate (STA) workloads—like the abstraction of routines popular now in smart homes, but applied to larger-scale edge IoT deployments. We present CoMesh, which tackles the challenge of building a decentralized mesh-based control plane for local, non-cloud, and hubless management of sense-trigger-actuate applications. CoMesh builds atop an abstraction called the coterie, which spreads STA load in a fine-grained way both across space and across time. A coterie uses a novel combination of techniques such as zero-message-exchange protocols (for fast proactive member selection), quorum-based agreement, and locality-sensitive hashing. We analyze and theoretically prove safety and liveness properties of CoMesh. Our evaluation with both a Raspberry Pi-4 deployment and larger-scale simulations, using real building maps and real routine workloads, shows that CoMesh is load-balanced, fast, fault-tolerant, and scalable.
{"title":"There is More Control in Egalitarian Edge IoT Meshes","authors":"Anna Karanika;Rui Yang;Xiaojuan Ma;Jiangran Wang;Shalni Sundram;Indranil Gupta","doi":"10.1109/TNSM.2025.3608796","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3608796","url":null,"abstract":"While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications with commonplace sense-trigger-actuate (STA) workloads—like the abstraction of routines popular now in smart homes, but applied to larger-scale edge IoT deployments. We present CoMesh, which tackles the challenge of building a decentralized mesh-based control plane for local, non-cloud, and hubless management of sense-trigger-actuate applications. CoMesh builds atop an abstraction called the coterie, which spreads STA load in a fine-grained way both across space and across time. A coterie uses a novel combination of techniques such as zero-message-exchange protocols (for fast proactive member selection), quorum-based agreement, and locality-sensitive hashing. We analyze and theoretically prove safety and liveness properties of CoMesh. Our evaluation with both a Raspberry Pi-4 deployment and larger-scale simulations, using real building maps and real routine workloads, shows that CoMesh is load-balanced, fast, fault-tolerant, and scalable.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"896-909"},"PeriodicalIF":5.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11159320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852462","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 : 2025-09-10DOI: 10.1109/TNSM.2025.3608074
Wei-Kun Chen;Ya-Feng Liu;Yu-Hong Dai;Zhi-Quan Luo
In this paper, we consider the network slicing (NS) problem which aims to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and manage network resources to meet diverse quality of service (QoS) requirements. We propose a mixed-integer nonlinear programming (MINLP) formulation for the considered NS problem that can flexibly route the traffic flow of the services on multiple paths and provide end-to-end delay and reliability guarantees for all services. To overcome the computational difficulty due to the intrinsic nonlinearity in the MINLP formulation, we transform the MINLP formulation into an equivalent mixed-integer linear programming (MILP) formulation and further show that their continuous relaxations are equivalent. In sharp contrast to the continuous relaxation of the MINLP formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the MILP formulation is a polynomial-time solvable linear programming problem, which significantly facilitates the algorithmic design. Based on the newly proposed MILP formulation, we develop a customized column generation (cCG) algorithm for solving the NS problem. The proposed cCG algorithm is a decomposition-based algorithm and is particularly suitable for solving large-scale NS problems. Numerical results demonstrate the efficacy of the proposed formulations and the proposed cCG algorithm.
{"title":"QoS-Aware and Routing-Flexible Network Slicing for Service-Oriented Networks","authors":"Wei-Kun Chen;Ya-Feng Liu;Yu-Hong Dai;Zhi-Quan Luo","doi":"10.1109/TNSM.2025.3608074","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3608074","url":null,"abstract":"In this paper, we consider the network slicing (NS) problem which aims to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and manage network resources to meet diverse quality of service (QoS) requirements. We propose a mixed-integer nonlinear programming (MINLP) formulation for the considered NS problem that can flexibly route the traffic flow of the services on multiple paths and provide end-to-end delay and reliability guarantees for all services. To overcome the computational difficulty due to the intrinsic nonlinearity in the MINLP formulation, we transform the MINLP formulation into an equivalent mixed-integer linear programming (MILP) formulation and further show that their continuous relaxations are equivalent. In sharp contrast to the continuous relaxation of the MINLP formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the MILP formulation is a polynomial-time solvable linear programming problem, which significantly facilitates the algorithmic design. Based on the newly proposed MILP formulation, we develop a customized column generation (cCG) algorithm for solving the NS problem. The proposed cCG algorithm is a decomposition-based algorithm and is particularly suitable for solving large-scale NS problems. Numerical results demonstrate the efficacy of the proposed formulations and the proposed cCG algorithm.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 6","pages":"6021-6036"},"PeriodicalIF":5.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665790","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 : 2025-09-08DOI: 10.1109/TNSM.2025.3607004
Xinhan Liu;Robert Kooij;Piet Van Mieghem
The node-reliability polynomial $nRel_{G}(p)$ measures the probability that a connected network remains connected given that each node functions independently with probability $p$ . Computing node-reliability polynomials $nRel_{G}(p)$ exactly is NP-hard. Here we propose efficient approximations. First, we develop an accurate Monte Carlo simulation, which is accelerated by incorporating a Laplace approximation that captures the polynomial’s main behavior. We also introduce three degree-based stochastic approximations (Laplace, arithmetic, and geometric), which leverage the degree distribution to estimate $nRel_{G}(p)$ with low complexity. Beyond approximations, our framework addresses the reliability-based Global Robustness Improvement Problem ($k$ -GRIP) by selecting exactly $k$ links to add to a given graph so as to maximize its node reliability. A Greedy Lowest-Degree Pairing Link Addition (Greedy-LD) Algorithm, is proposed which offers a computationally efficient and practically effective heuristic, particularly suitable for large-scale networks.
{"title":"Node-Reliability: Monte Carlo, Laplace, and Stochastic Approximations and a Greedy Link-Augmentation Strategy","authors":"Xinhan Liu;Robert Kooij;Piet Van Mieghem","doi":"10.1109/TNSM.2025.3607004","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3607004","url":null,"abstract":"The node-reliability polynomial <inline-formula> <tex-math>$nRel_{G}(p)$ </tex-math></inline-formula> measures the probability that a connected network remains connected given that each node functions independently with probability <inline-formula> <tex-math>$p$ </tex-math></inline-formula>. Computing node-reliability polynomials <inline-formula> <tex-math>$nRel_{G}(p)$ </tex-math></inline-formula> exactly is NP-hard. Here we propose efficient approximations. First, we develop an accurate Monte Carlo simulation, which is accelerated by incorporating a Laplace approximation that captures the polynomial’s main behavior. We also introduce three degree-based stochastic approximations (Laplace, arithmetic, and geometric), which leverage the degree distribution to estimate <inline-formula> <tex-math>$nRel_{G}(p)$ </tex-math></inline-formula> with low complexity. Beyond approximations, our framework addresses the reliability-based Global Robustness Improvement Problem (<inline-formula> <tex-math>$k$ </tex-math></inline-formula>-GRIP) by selecting exactly <inline-formula> <tex-math>$k$ </tex-math></inline-formula> links to add to a given graph so as to maximize its node reliability. A Greedy Lowest-Degree Pairing Link Addition (Greedy-LD) Algorithm, is proposed which offers a computationally efficient and practically effective heuristic, particularly suitable for large-scale networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"756-766"},"PeriodicalIF":5.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852507","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 : 2025-08-29DOI: 10.1109/TNSM.2025.3603597
Shiqi Zhang;Mridul Gupta;Behnam Dezfouli
As the number of WiFi devices and their traffic demands continue to rise, the need for a scalable and high-performance wireless infrastructure becomes increasingly essential. Central to this infrastructure are WiFi Access Points (APs), which facilitate packet switching between Ethernet and WiFi interfaces. Despite APs’ reliance on the Linux kernel’s data plane for packet switching, the detailed operations and complexities of switching packets between Ethernet and WiFi interfaces have not been investigated in existing works. This paper makes the following contributions towards filling this research gap. Through macro and micro-analysis of empirical experiments, our study reveals insights in two distinct categories. Firstly, while the kernel’s statistics offer valuable insights into system operations, we identify and discuss potential pitfalls that can severely affect system analysis. For instance, we reveal how packet switching rate and the implementation of drivers influence the meaning and accuracy of statistics related to packet-switching tasks and processor utilization. Secondly, we analyze the impact of the packet switching path and core configuration on performance and power consumption. Specifically, we identify the differences in Ethernet-to-WiFi and WiFi-to-Ethernet data paths regarding processing components, multi-core utilization, and energy efficiency.
{"title":"Understanding Linux Kernel-Based Packet Switching on WiFi Access Points","authors":"Shiqi Zhang;Mridul Gupta;Behnam Dezfouli","doi":"10.1109/TNSM.2025.3603597","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3603597","url":null,"abstract":"As the number of WiFi devices and their traffic demands continue to rise, the need for a scalable and high-performance wireless infrastructure becomes increasingly essential. Central to this infrastructure are WiFi Access Points (APs), which facilitate packet switching between Ethernet and WiFi interfaces. Despite APs’ reliance on the Linux kernel’s data plane for packet switching, the detailed operations and complexities of switching packets between Ethernet and WiFi interfaces have not been investigated in existing works. This paper makes the following contributions towards filling this research gap. Through macro and micro-analysis of empirical experiments, our study reveals insights in two distinct categories. Firstly, while the kernel’s statistics offer valuable insights into system operations, we identify and discuss potential pitfalls that can severely affect system analysis. For instance, we reveal how packet switching rate and the implementation of drivers influence the meaning and accuracy of statistics related to packet-switching tasks and processor utilization. Secondly, we analyze the impact of the packet switching path and core configuration on performance and power consumption. Specifically, we identify the differences in Ethernet-to-WiFi and WiFi-to-Ethernet data paths regarding processing components, multi-core utilization, and energy efficiency.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"3792-3808"},"PeriodicalIF":5.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315532","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 : 2025-08-28DOI: 10.1109/TNSM.2025.3603630
Wenjun Fan;Na Fan;Junhui Zhang;Jia Liu;Yifan Dai
On vehicular named data network (VNDN), Interest Flooding Attack (IFA) can exhaust the computing resources by sending a large number of malicious Interest packets, which leads to the failure of satisfying the legitimate requests and seriously hazards the operation of Internet of Vehicles (IoV). To solve this problem, this paper proposes a distributed network traffic monitoring-enabled multi-indicator detection and prevention approach for VNDN to detect and resist the IFA attacks. In order for facilitating this approach, a distributed network traffic monitoring layer based on road side unit (RSU) is constructed. With such a monitoring layer, a multi-indicator detection approach is designed, which consists of three indicators: information entropy, self-similarity, and singularity, whereby the thresholds are tweaked by the real-time density of traffic flow. Apart from the detection, a blacklisting based prevention approach is realized to mitigate the attack impact. We validate the proposed approach via prototyping it on our VNDN experimental platform using realistic parameters setting and leveraging the original NDN packet structure to corroborate the usage of the required Source ID for identifying the source of the Interest packet, which consolidates the practicability of the approach. The experimental results show that our multi-indicator detection approach has a greatly higher detection performance than those of using indicators individually, and the blacklisting-based prevention can effectively mitigate the attack impact as well.
{"title":"Securing VNDN With Multi-Indicator Intrusion Detection Approach Against the IFA Threat","authors":"Wenjun Fan;Na Fan;Junhui Zhang;Jia Liu;Yifan Dai","doi":"10.1109/TNSM.2025.3603630","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3603630","url":null,"abstract":"On vehicular named data network (VNDN), Interest Flooding Attack (IFA) can exhaust the computing resources by sending a large number of malicious Interest packets, which leads to the failure of satisfying the legitimate requests and seriously hazards the operation of Internet of Vehicles (IoV). To solve this problem, this paper proposes a distributed network traffic monitoring-enabled multi-indicator detection and prevention approach for VNDN to detect and resist the IFA attacks. In order for facilitating this approach, a distributed network traffic monitoring layer based on road side unit (RSU) is constructed. With such a monitoring layer, a multi-indicator detection approach is designed, which consists of three indicators: information entropy, self-similarity, and singularity, whereby the thresholds are tweaked by the real-time density of traffic flow. Apart from the detection, a blacklisting based prevention approach is realized to mitigate the attack impact. We validate the proposed approach via prototyping it on our VNDN experimental platform using realistic parameters setting and leveraging the original NDN packet structure to corroborate the usage of the required Source ID for identifying the source of the Interest packet, which consolidates the practicability of the approach. The experimental results show that our multi-indicator detection approach has a greatly higher detection performance than those of using indicators individually, and the blacklisting-based prevention can effectively mitigate the attack impact as well.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 6","pages":"6097-6111"},"PeriodicalIF":5.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665762","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 : 2025-08-26DOI: 10.1109/TNSM.2025.3602964
Shuangwu Chen;Siyang Chen;Yuxing Wei;Dong Jin;Xiaobin Tan;Xiaofeng Jiang;Jian Yang
Website Fingerprinting (WF) attacks have posed a serious threat to the anonymity of the onion router (Tor) communication system, as attackers can passively pry into the encrypted traffic and infer the website visited by users. To defend against WF, recent studies focus on adversarial perturbations. However, most of them suffer from a high bandwidth overhead and a low defense performance. To address this problem, our basic idea is to generate perturbation only on the sensitive regions, which can effectively mask the website’s fingerprint, thus misleading the WF attack models and reducing the bandwidth overhead. In this paper, we formulate a joint optimization problem of perturbation position and magnitude by confining the perturbations within sensitive regions, which is rarely considered in the literature. We propose a robust low-overhead WF defense method based on reinforcement learning (RL), named RLpatch. RLpatch identifies the common sensitive regions of various surrogate models and adjusts perturbation according to the query result from a query WF model. It further employs the positional frequency of perturbations to generate a common perturbation paradigm for different traces of a same website. Experimental results show that RLpatch achieves higher defense performance, lower bandwidth overhead and better robustness against adversarial training compared to the state-of-the-art methods.
{"title":"RLpatch: A Robust Low-Overhead Website Fingerprinting Defense Method Based on Reinforcement Learning Within Sensitive Regions","authors":"Shuangwu Chen;Siyang Chen;Yuxing Wei;Dong Jin;Xiaobin Tan;Xiaofeng Jiang;Jian Yang","doi":"10.1109/TNSM.2025.3602964","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3602964","url":null,"abstract":"Website Fingerprinting (WF) attacks have posed a serious threat to the anonymity of the onion router (Tor) communication system, as attackers can passively pry into the encrypted traffic and infer the website visited by users. To defend against WF, recent studies focus on adversarial perturbations. However, most of them suffer from a high bandwidth overhead and a low defense performance. To address this problem, our basic idea is to generate perturbation only on the sensitive regions, which can effectively mask the website’s fingerprint, thus misleading the WF attack models and reducing the bandwidth overhead. In this paper, we formulate a joint optimization problem of perturbation position and magnitude by confining the perturbations within sensitive regions, which is rarely considered in the literature. We propose a robust low-overhead WF defense method based on reinforcement learning (RL), named RLpatch. RLpatch identifies the common sensitive regions of various surrogate models and adjusts perturbation according to the query result from a query WF model. It further employs the positional frequency of perturbations to generate a common perturbation paradigm for different traces of a same website. Experimental results show that RLpatch achieves higher defense performance, lower bandwidth overhead and better robustness against adversarial training compared to the state-of-the-art methods.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 6","pages":"6066-6082"},"PeriodicalIF":5.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665810","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 : 2025-08-25DOI: 10.1109/TNSM.2025.3602646
Amit Kumar Bhuyan;Hrishikesh Dutta;Subir Biswas
This paper presents a Micro-Unmanned Aerial Vehicle (UAV)-enhanced content management system for disaster scenarios where communication infrastructure is generally compromised. Utilizing a hybrid network of stationary and mobile Micro-UAVs, this system aims to provide crucial content access to isolated communities. In the developed architecture, stationary anchor UAVs, equipped with vertical and lateral links, serve users in individual disaster-affected communities. and mobile micro-ferrying UAVs, with enhanced mobility, extend coverage across multiple such communities. The primary goal is to devise a content dissemination system that dynamically learns caching policies to maximize content accessibility to users left without communication infrastructure. The core contribution is an adaptive content dissemination framework that employs a decentralized Top-k Multi-Armed Bandit learning approach for efficient UAV caching decisions. This approach accounts for geo-temporal variations in content popularity and diverse user demands. Additionally, a Selective Caching Algorithm is proposed to minimize redundant content copies by leveraging inter-UAV information sharing. Through functional verification and performance evaluation, the proposed framework demonstrates improved system performance and adaptability across varying network sizes, micro-UAV swarms, and content popularity distributions.
{"title":"Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs","authors":"Amit Kumar Bhuyan;Hrishikesh Dutta;Subir Biswas","doi":"10.1109/TNSM.2025.3602646","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3602646","url":null,"abstract":"This paper presents a Micro-Unmanned Aerial Vehicle (UAV)-enhanced content management system for disaster scenarios where communication infrastructure is generally compromised. Utilizing a hybrid network of stationary and mobile Micro-UAVs, this system aims to provide crucial content access to isolated communities. In the developed architecture, stationary anchor UAVs, equipped with vertical and lateral links, serve users in individual disaster-affected communities. and mobile micro-ferrying UAVs, with enhanced mobility, extend coverage across multiple such communities. The primary goal is to devise a content dissemination system that dynamically learns caching policies to maximize content accessibility to users left without communication infrastructure. The core contribution is an adaptive content dissemination framework that employs a decentralized Top-k Multi-Armed Bandit learning approach for efficient UAV caching decisions. This approach accounts for geo-temporal variations in content popularity and diverse user demands. Additionally, a Selective Caching Algorithm is proposed to minimize redundant content copies by leveraging inter-UAV information sharing. Through functional verification and performance evaluation, the proposed framework demonstrates improved system performance and adaptability across varying network sizes, micro-UAV swarms, and content popularity distributions.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 6","pages":"6229-6244"},"PeriodicalIF":5.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665782","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 : 2025-08-18DOI: 10.1109/TNSM.2025.3599168
Huigyu Yang;Jeongjun Park;Syed M. Raza;Moonseong Kim;Min Young Chung;Hyunseung Choo
The mobile network traffic patterns in urban areas significantly diverge depending on commercial and residential establishments. These regional traffic patterns provide crucial clues for predicting traffic patterns precisely. Previous studies have employed a combination of time-series and convolutional Deep Learning (DL) models to effectively capture the correlation of the regional features and traffic patterns. Despite promising results, these approaches are limited in identifying pattern similarities among sparsely located regions and can be further improved. To this end, this study proposes a GEospatial clustering and residual Convolutional temporal long Short-term memory (GECOS) framework consisting of clustering and DL components. The proposed Urbanflow Peak Clustering (UPC) component exploits the peak traffic times of daily mobile data to obtain the groups of cells with similar traffic patterns apart from their geographical diversity. The UPC improves the scalability of existing algorithms and enables DL components to improve their accuracy by recognizing unique regional patterns and localizing the training targets. The proposed Residual Convolutional TCN-LSTM (RCTL) serves as the DL component of GECOS that improves TCN-LSTM structure through layer-wise feature transfer and enhances long-term dependency learnability. The RCTL ensures more accurate capturing of extensive spatiotemporal features through structural enhancements. The experiments conducted on real-world mobile traffic data showcase 43% improvement by GECOS compared to state-of-the-art models, enabling precise traffic engineering policies by operators.
{"title":"Urban Mobile Data Prediction With Geospatial Clustering and Dual Residual Learning","authors":"Huigyu Yang;Jeongjun Park;Syed M. Raza;Moonseong Kim;Min Young Chung;Hyunseung Choo","doi":"10.1109/TNSM.2025.3599168","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3599168","url":null,"abstract":"The mobile network traffic patterns in urban areas significantly diverge depending on commercial and residential establishments. These regional traffic patterns provide crucial clues for predicting traffic patterns precisely. Previous studies have employed a combination of time-series and convolutional Deep Learning (DL) models to effectively capture the correlation of the regional features and traffic patterns. Despite promising results, these approaches are limited in identifying pattern similarities among sparsely located regions and can be further improved. To this end, this study proposes a GEospatial clustering and residual Convolutional temporal long Short-term memory (GECOS) framework consisting of clustering and DL components. The proposed Urbanflow Peak Clustering (UPC) component exploits the peak traffic times of daily mobile data to obtain the groups of cells with similar traffic patterns apart from their geographical diversity. The UPC improves the scalability of existing algorithms and enables DL components to improve their accuracy by recognizing unique regional patterns and localizing the training targets. The proposed Residual Convolutional TCN-LSTM (RCTL) serves as the DL component of GECOS that improves TCN-LSTM structure through layer-wise feature transfer and enhances long-term dependency learnability. The RCTL ensures more accurate capturing of extensive spatiotemporal features through structural enhancements. The experiments conducted on real-world mobile traffic data showcase 43% improvement by GECOS compared to state-of-the-art models, enabling precise traffic engineering policies by operators.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 6","pages":"6260-6273"},"PeriodicalIF":5.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665789","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 : 2025-08-18DOI: 10.1109/TNSM.2025.3599393
Mohammad Saleh Mahdizadeh;Behnam Bahrak;Mohammad Sayad Haghighi
The Bitcoin Lightning Network, as a second-layer solution for enhancing the scalability of Bitcoin transactions, facilitates transactions through payment channels between nodes. However, the rapid growth of the network and rising transaction volumes have exacerbated the challenge of managing payment channel imbalances. Payment channel imbalance, characterized by the concentration of liquidity in one direction, leads to a decrease in payment success rates, a reduction in the effective lifespan of payment channels, and a decline in the network’s overall efficiency and throughput. This study introduces a graph neural network-based recommendation strategy designed to enhance the Lightning Network’s autopilot system. The proposed approach proactively mitigates channel imbalances by optimizing channel recommendations, enabling dynamic and scalable liquidity management for network users. Simulations conducted using the CLoTH tool demonstrate a 45% increase in payment success rates, a 46% reduction in imbalanced channels, and a 14% increase in the lifespan of payment channels across the network compared to the existing autopilot recommendation strategies, and when compared with the commonly adopted circular rebalancing method, the proposed strategy achieves a 27% improvement in payment success rates. Additionally, we offer a comparative topological analysis between two snapshots of the LN, taken in November 2021 and August 2023, to facilitate unsupervised learning tasks. The results highlight an increase in network centralization alongside a decrease in the network size, emphasizing the growing need for decentralization strategies in the LN, such as the one proposed in this study.
{"title":"A GNN-Based Autopilot Recommendation Strategy to Mitigate Payment Channel Imbalance Problem in Bitcoin Lightning Network","authors":"Mohammad Saleh Mahdizadeh;Behnam Bahrak;Mohammad Sayad Haghighi","doi":"10.1109/TNSM.2025.3599393","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3599393","url":null,"abstract":"The Bitcoin Lightning Network, as a second-layer solution for enhancing the scalability of Bitcoin transactions, facilitates transactions through payment channels between nodes. However, the rapid growth of the network and rising transaction volumes have exacerbated the challenge of managing payment channel imbalances. Payment channel imbalance, characterized by the concentration of liquidity in one direction, leads to a decrease in payment success rates, a reduction in the effective lifespan of payment channels, and a decline in the network’s overall efficiency and throughput. This study introduces a graph neural network-based recommendation strategy designed to enhance the Lightning Network’s autopilot system. The proposed approach proactively mitigates channel imbalances by optimizing channel recommendations, enabling dynamic and scalable liquidity management for network users. Simulations conducted using the CLoTH tool demonstrate a 45% increase in payment success rates, a 46% reduction in imbalanced channels, and a 14% increase in the lifespan of payment channels across the network compared to the existing autopilot recommendation strategies, and when compared with the commonly adopted circular rebalancing method, the proposed strategy achieves a 27% improvement in payment success rates. Additionally, we offer a comparative topological analysis between two snapshots of the LN, taken in November 2021 and August 2023, to facilitate unsupervised learning tasks. The results highlight an increase in network centralization alongside a decrease in the network size, emphasizing the growing need for decentralization strategies in the LN, such as the one proposed in this study.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"1863-1873"},"PeriodicalIF":5.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982125","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：