Pub Date : 2024-12-30DOI: 10.1109/TNSE.2024.3524362
Shangshang Wang;Ziyu Shao;Yang Yang
Bandit is acknowledged as a classical analytic tool for the online decision-making problem under uncertainty, e.g., task assignment for crowdsourcing systems given the unknown reliability of workers. In the conventional setup, an agent selects from a set of arms across rounds to balance the exploitation-exploration tradeoff using quantitive reward feedback. Despite bandits' popularity, their practical implementation may run into concerns like 1) obtaining the quantitive reward is a non-trivial problem, e.g., evaluating workers' completion quality (reward) requires domain experts to set up metrics; 2) mismatch between the budgeted agent and costs for selecting arms, e.g., the crowdsourcing platform (agent) should offer payments (cost) to workers to complete tasks. To address such concerns, 1) we employ dueling bandits to learn the uncertainties via qualitative pairwise comparisons rather than quantitive rewards, e.g., whether a worker performs better on the assigned task than the other; 2) we utilize online control to guarantee a within-budget cost while selecting arms. By integrating online learning and online control, we propose a Constrained Two-Dueling Bandit (CTDB) algorithm. We prove that CTDB achieves a $O(1/V + sqrt{log T / T})$ round-averaged regret over the horizon $T$ while keeping a budgeted cost where $V$ is a constant parameter balancing the tradeoff between regret minimization and constraint satisfaction. We conduct extensive simulations with synthetic and real-world datasets to demonstrate the outperformance of CTDB over baselines.
{"title":"Constrained Dueling Bandits for Edge Intelligence","authors":"Shangshang Wang;Ziyu Shao;Yang Yang","doi":"10.1109/TNSE.2024.3524362","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3524362","url":null,"abstract":"Bandit is acknowledged as a classical analytic tool for the online decision-making problem under uncertainty, e.g., task assignment for crowdsourcing systems given the unknown reliability of workers. In the conventional setup, an agent selects from a set of arms across rounds to balance the exploitation-exploration tradeoff using quantitive reward feedback. Despite bandits' popularity, their practical implementation may run into concerns like 1) obtaining the quantitive reward is a non-trivial problem, e.g., evaluating workers' completion quality (reward) requires domain experts to set up metrics; 2) mismatch between the budgeted agent and costs for selecting arms, e.g., the crowdsourcing platform (agent) should offer payments (cost) to workers to complete tasks. To address such concerns, 1) we employ dueling bandits to learn the uncertainties via qualitative pairwise comparisons rather than quantitive rewards, e.g., whether a worker performs better on the assigned task than the other; 2) we utilize online control to guarantee a within-budget cost while selecting arms. By integrating online learning and online control, we propose a <italic>Constrained Two-Dueling Bandit (CTDB)</i> algorithm. We prove that CTDB achieves a <inline-formula><tex-math>$O(1/V + sqrt{log T / T})$</tex-math></inline-formula> round-averaged regret over the horizon <inline-formula><tex-math>$T$</tex-math></inline-formula> while keeping a budgeted cost where <inline-formula><tex-math>$V$</tex-math></inline-formula> is a constant parameter balancing the tradeoff between regret minimization and constraint satisfaction. We conduct extensive simulations with synthetic and real-world datasets to demonstrate the outperformance of CTDB over baselines.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1126-1136"},"PeriodicalIF":6.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465699","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 this paper, we investigate a distributed optimization problem in multi-agent systems, where the cost function is a sum of local cost functions associated with individual agents. Inspired by the outstanding performance of proportional-integral-derivative (PID) controllers in the field of control, we propose the Distributed PID Optimization Algorithm (D-PID) based on output feedback to solve the distributed optimization problem. We aim to establish the exponential convergence of the D-PID algorithm over undirected connected graphs when the local objective functions are smooth and strongly convex. Additionally, we provide guidelines for selecting appropriate parameter values (e.g., $K_{p}, K_{i}$, and $K_{d}$) and analyze the correctness of the algorithm over time-varying interaction graphs. To further reduce unnecessary communication resource consumption, we develop the Distributed PID Optimization Algorithm with Time-Triggered Scheme (D-PID-TT). We theoretically demonstrate that D-PID-TT can converge to an optimal solution at a global exponential convergence rate under the same conditions as D-PID. We also provide guidelines for parameter selection and specify the communication period. Furthermore, we show that the D-PID has great potential for nonconvex distributed optimization. Finally, we present numerical simulations to verify the effectiveness and superiority of our proposed algorithms.
{"title":"Output Feedback-Based Continuous-Time Distributed PID Optimization Algorithms","authors":"Jiaxu Liu;Song Chen;Pengkai Wang;Shengze Cai;Chao Xu;Jian Chu","doi":"10.1109/TNSE.2024.3521587","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521587","url":null,"abstract":"In this paper, we investigate a distributed optimization problem in multi-agent systems, where the cost function is a sum of local cost functions associated with individual agents. Inspired by the outstanding performance of proportional-integral-derivative (PID) controllers in the field of control, we propose the Distributed PID Optimization Algorithm (D-PID) based on output feedback to solve the distributed optimization problem. We aim to establish the exponential convergence of the D-PID algorithm over undirected connected graphs when the local objective functions are smooth and strongly convex. Additionally, we provide guidelines for selecting appropriate parameter values (e.g., <inline-formula><tex-math>$K_{p}, K_{i}$</tex-math></inline-formula>, and <inline-formula><tex-math>$K_{d}$</tex-math></inline-formula>) and analyze the correctness of the algorithm over time-varying interaction graphs. To further reduce unnecessary communication resource consumption, we develop the Distributed PID Optimization Algorithm with Time-Triggered Scheme (D-PID-TT). We theoretically demonstrate that D-PID-TT can converge to an optimal solution at a global exponential convergence rate under the same conditions as D-PID. We also provide guidelines for parameter selection and specify the communication period. Furthermore, we show that the D-PID has great potential for nonconvex distributed optimization. Finally, we present numerical simulations to verify the effectiveness and superiority of our proposed algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"955-969"},"PeriodicalIF":6.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465673","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}
As the first service procedure in crowdsourcing, task matching is crucial for users and has aroused extensive attention. However, due to the submission of sensitive information, task requesters and workers have growing concerns about matching security and privacy, as well as efficiency and flexibility for service quality. Prior privacy-aware task-matching resolutions either rely on a central semi-honest crowdsourcing platform for matching integrity, or still suffer from low efficiency, limited privacy considerations, and inflexibility even if blockchain is incorporated for decentralized matching. In this paper, we construct a decentralized, secure, and flexibly expressive crowdsourcing task-matching system robust to misbehaviors based on consortium blockchain. Particularly, to support fine-grained worker selection and worker-side task search with dual-side privacy under no central trust, we propose a multi-authority policy-hiding attribute-based encryption scheme with keyword search, enforced by smart contracts. We optimize the ciphertext and key size by designing a novel approach for policy and attribute vector generation, meanwhile immune to malicious workers submitting incorrect vectors. Such a verifiable vector generation approach exploits verifiable multiplicative homomorphic secret sharing and Viète's formulas. Formal security analysis and extensive experiments conducted over Hyperledger Fabric demonstrate the desired security properties and superior on-chain and off-chain performance.
{"title":"Matching as You Want: A Decentralized, Flexible, and Efficient Realization for Crowdsourcing With Dual-Side Privacy","authors":"Liang Li;Haiqin Wu;Liangen He;Jucai Yang;Zhenfu Cao;Boris Düdder","doi":"10.1109/TNSE.2024.3522914","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3522914","url":null,"abstract":"As the first service procedure in crowdsourcing, task matching is crucial for users and has aroused extensive attention. However, due to the submission of sensitive information, task requesters and workers have growing concerns about matching security and privacy, as well as efficiency and flexibility for service quality. Prior privacy-aware task-matching resolutions either rely on a central semi-honest crowdsourcing platform for matching integrity, or still suffer from low efficiency, limited privacy considerations, and inflexibility even if blockchain is incorporated for decentralized matching. In this paper, we construct a decentralized, secure, and flexibly expressive crowdsourcing task-matching system robust to misbehaviors based on consortium blockchain. Particularly, to support fine-grained worker selection and worker-side task search with dual-side privacy under no central trust, we propose a multi-authority policy-hiding attribute-based encryption scheme with keyword search, enforced by smart contracts. We optimize the ciphertext and key size by designing a novel approach for policy and attribute vector generation, meanwhile immune to malicious workers submitting incorrect vectors. Such a verifiable vector generation approach exploits verifiable multiplicative homomorphic secret sharing and Viète's formulas. Formal security analysis and extensive experiments conducted over Hyperledger Fabric demonstrate the desired security properties and superior on-chain and off-chain performance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1026-1040"},"PeriodicalIF":6.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464341","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 this paper, a resilient hybrid event-triggered (RHET) scheme is presented to deal with the secure synchronization issue of Lur'e systems to resist denial-of-service (DoS) attacks. The causes of considering this issue are threefold: 1) networked control systems (NCSs) are always limited by the network bandwidth and are vulnerable to DoS attacks 2) the existing models cannot describe the RHET control systems under DoS attacks and 3) the existing functionals are either inappropriate or conservative for addressing the secure synchronization problem. To overcome these challenges, an RHET scheme which is integrates of the sampled-data control and the continuous event-triggered control, is firstly proposed to reduce the effect of DoS attacks as well as the number of data release. On this basis, a switched closed-loop system model for Lur'e systems under RHET control and DoS attacks is established. By fully employing the state information in the sampling interval and the attack interval, two multi-interval-dependent functionals are designed to conduct the stability analysis. Subsequently, the continuity of the constructed functionals, the convex combination technique and some estimation techniques are jointly utilized to derive the exponential synchronization results and to design a secure controller. Finally, two simulation examples, including a hyperchaotic system and a neural network, are utilized to testify the effectiveness of the presented RHET control method in achieving the secure synchronization.
{"title":"Resilient Hybrid Event-Triggered Control for Secure Synchronization of Lur'e Systems Against DoS Attacks","authors":"Yanyan Ni;Zhen Wang;Yingjie Fan;Xia Huang;Hao Shen","doi":"10.1109/TNSE.2024.3522991","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3522991","url":null,"abstract":"In this paper, a resilient hybrid event-triggered (RHET) scheme is presented to deal with the secure synchronization issue of Lur'e systems to resist denial-of-service (DoS) attacks. The causes of considering this issue are threefold: 1) networked control systems (NCSs) are always limited by the network bandwidth and are vulnerable to DoS attacks 2) the existing models cannot describe the RHET control systems under DoS attacks and 3) the existing functionals are either inappropriate or conservative for addressing the secure synchronization problem. To overcome these challenges, an RHET scheme which is integrates of the sampled-data control and the continuous event-triggered control, is firstly proposed to reduce the effect of DoS attacks as well as the number of data release. On this basis, a switched closed-loop system model for Lur'e systems under RHET control and DoS attacks is established. By fully employing the state information in the sampling interval and the attack interval, two multi-interval-dependent functionals are designed to conduct the stability analysis. Subsequently, the continuity of the constructed functionals, the convex combination technique and some estimation techniques are jointly utilized to derive the exponential synchronization results and to design a secure controller. Finally, two simulation examples, including a hyperchaotic system and a neural network, are utilized to testify the effectiveness of the presented RHET control method in achieving the secure synchronization.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1053-1065"},"PeriodicalIF":6.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465582","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}
Simultaneous tensor communication can effectively improve the scalability of distributed deep learning on large clusters. However, a fixed number of tensor blocks communicated concurrently violates the priority-based scheduling strategy and cannot minimize communication overheads. In this paper, we propose a novel simultaneous tensor communication framework, namely D-Credit, which transmits tensor blocks based on dynamic sliding windows to minimize per-iteration time in distributed DNN training. We build the mathematical model of D-Credit in two phases: (1) the overlap of gradient communication and backward propagation, and (2) the overlap of gradient communication and forward computation. We drive the optimal window sizes for the second phase analytically, and develop a greedy algorithm to efficiently determine the dynamic window sizes for the first phase of D-Credit. We implement the D-Credit architecture on PyTorch framework. Experimental results on two different GPU clusters demonstrate that at training speed, D-Credit can achieve up to 1.26x, 1.21x, 1.48x and 1.53x speedup compared to ByteScheduler, DeAR, PyTorch-DDP and WFBP, respectively. At energy consumption, D-Credit saves up to 17.8% and 25.1% of the training energy consumption compared to ByteScheduler and WFBP, respectively.
{"title":"A Dynamic Sliding Window Based Tensor Communication Scheduling Framework for Distributed Deep Learning","authors":"Yunqi Gao;Bing Hu;Mahdi Boloursaz Mashhadi;Wei Wang;Rahim Tafazolli;Mérouane Debbah","doi":"10.1109/TNSE.2024.3523320","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3523320","url":null,"abstract":"Simultaneous tensor communication can effectively improve the scalability of distributed deep learning on large clusters. However, a fixed number of tensor blocks communicated concurrently violates the priority-based scheduling strategy and cannot minimize communication overheads. In this paper, we propose a novel simultaneous tensor communication framework, namely D-Credit, which transmits tensor blocks based on dynamic sliding windows to minimize per-iteration time in distributed DNN training. We build the mathematical model of D-Credit in two phases: (1) the overlap of gradient communication and backward propagation, and (2) the overlap of gradient communication and forward computation. We drive the optimal window sizes for the second phase analytically, and develop a greedy algorithm to efficiently determine the dynamic window sizes for the first phase of D-Credit. We implement the D-Credit architecture on PyTorch framework. Experimental results on two different GPU clusters demonstrate that at training speed, D-Credit can achieve up to 1.26x, 1.21x, 1.48x and 1.53x speedup compared to ByteScheduler, DeAR, PyTorch-DDP and WFBP, respectively. At energy consumption, D-Credit saves up to 17.8% and 25.1% of the training energy consumption compared to ByteScheduler and WFBP, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1080-1095"},"PeriodicalIF":6.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464344","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 : 2024-12-26DOI: 10.1109/TNSE.2024.3521398
Yulong Chen;Bo Yin;Alia Asheralieva;Xuetao Wei
Blockchain is a promising technology that ensures data integrity for applications. However, the expensive cost of blockchain storage makes putting all data on-chain impossible. Hybrid-storage blockchain (HSB) can alleviate this problem by storing the data content off-chain and the data hash on-chain while using an authenticated data structure (ADS) in the smart contract to ensure the integrity of data storage and retrieval. The key challenge in HSB is minimizing the maintenance cost of ADS on-chain. In this paper, we focus on the authenticated range query in HSB and propose a new scheme named UpOnFly to efficiently maintain the root of the MB-tree as ADS for any dynamic changes without using all the data or storing internal nodes of the tree in the smart contract. Furthermore, without sacrificing much off-chain verification performance, we propose another new scheme named KeyLink that decouples the on-chain ADS and the off-chain index and only maintains the order of all data keys in the smart contract, in which the ADS maintenance cost is significantly reduced and will not increase with the dataset size. Extensive experimental results demonstrate that the UpOnFly scheme requires only $63%$ and $48%$ of the gas consumption of the state-of-the-art approach $GEM^{2*}$-tree with the dataset size of $10^6$ and $10^8$, respectively. The KeyLink scheme is even more efficient, requiring only $29%$ and $18%$ of the gas consumption of $GEM^{2*}$-tree with the dataset size of $10^6$ and $10^8$, respectively.
{"title":"New Gas-Efficient Authenticated Range Query Schemes in Hybrid-Storage Blockchain","authors":"Yulong Chen;Bo Yin;Alia Asheralieva;Xuetao Wei","doi":"10.1109/TNSE.2024.3521398","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521398","url":null,"abstract":"Blockchain is a promising technology that ensures data integrity for applications. However, the expensive cost of blockchain storage makes putting all data on-chain impossible. Hybrid-storage blockchain (HSB) can alleviate this problem by storing the data content off-chain and the data hash on-chain while using an authenticated data structure (ADS) in the smart contract to ensure the integrity of data storage and retrieval. The key challenge in HSB is minimizing the maintenance cost of ADS on-chain. In this paper, we focus on the authenticated range query in HSB and propose a new scheme named UpOnFly to efficiently maintain the root of the MB-tree as ADS for any dynamic changes without using all the data or storing internal nodes of the tree in the smart contract. Furthermore, without sacrificing much off-chain verification performance, we propose another new scheme named KeyLink that decouples the on-chain ADS and the off-chain index and only maintains the order of all data keys in the smart contract, in which the ADS maintenance cost is significantly reduced and will not increase with the dataset size. Extensive experimental results demonstrate that the UpOnFly scheme requires only <inline-formula><tex-math>$63%$</tex-math></inline-formula> and <inline-formula><tex-math>$48%$</tex-math></inline-formula> of the gas consumption of the state-of-the-art approach <inline-formula><tex-math>$GEM^{2*}$</tex-math></inline-formula>-tree with the dataset size of <inline-formula><tex-math>$10^6$</tex-math></inline-formula> and <inline-formula><tex-math>$10^8$</tex-math></inline-formula>, respectively. The KeyLink scheme is even more efficient, requiring only <inline-formula><tex-math>$29%$</tex-math></inline-formula> and <inline-formula><tex-math>$18%$</tex-math></inline-formula> of the gas consumption of <inline-formula><tex-math>$GEM^{2*}$</tex-math></inline-formula>-tree with the dataset size of <inline-formula><tex-math>$10^6$</tex-math></inline-formula> and <inline-formula><tex-math>$10^8$</tex-math></inline-formula>, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"925-942"},"PeriodicalIF":6.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465652","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 : 2024-12-25DOI: 10.1109/TNSE.2024.3521598
Lei Xue;Haoyu Zhou;Yongbao Wu;Jian Liu;Donald C. Wunsch
In this paper, the problem of practical predefined-time synchronization in mean square (PTSMS) of stochastic complex networks (SCNs) is investigated through dynamic event-triggered control (E-TC). Different from the existing literature, this paper considers the dynamic E-TC in an aperiodically intermittent control framework and employs the average control rate, which makes it easier to satisfy the conditions of the theorem. In comparison to existing finite-time and fixed-time synchronization, by introducing the time-varying function, it can be guaranteed that all states of SCNs achieve the practical PTSMS within a preset time without calculating the convergence time. Combined with stochastic analysis theory, the practical PTSMS criterion for aperiodically intermittent dynamic event-triggered control (AIDE-TC) is derived by constructing a Lyapunov function with an auxiliary function. In addition, all event generators for AIDE-TC proposed in this paper ensure a minimum inter-event interval for each sample path solution, thus excluding Zeno behavior. Finally, to demonstrate that the model in this paper can be applied to real-world networks, the theoretical results are verified by an application of the Kuramoto oscillator networks.
{"title":"Aperiodically Intermittent Dynamic Event-Triggered Control for Predefined-Time Synchronization of Stochastic Complex Networks","authors":"Lei Xue;Haoyu Zhou;Yongbao Wu;Jian Liu;Donald C. Wunsch","doi":"10.1109/TNSE.2024.3521598","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521598","url":null,"abstract":"In this paper, the problem of practical predefined-time synchronization in mean square (PTSMS) of stochastic complex networks (SCNs) is investigated through dynamic event-triggered control (E-TC). Different from the existing literature, this paper considers the dynamic E-TC in an aperiodically intermittent control framework and employs the average control rate, which makes it easier to satisfy the conditions of the theorem. In comparison to existing finite-time and fixed-time synchronization, by introducing the time-varying function, it can be guaranteed that all states of SCNs achieve the practical PTSMS within a preset time without calculating the convergence time. Combined with stochastic analysis theory, the practical PTSMS criterion for aperiodically intermittent dynamic event-triggered control (AIDE-TC) is derived by constructing a Lyapunov function with an auxiliary function. In addition, all event generators for AIDE-TC proposed in this paper ensure a minimum inter-event interval for each sample path solution, thus excluding Zeno behavior. Finally, to demonstrate that the model in this paper can be applied to real-world networks, the theoretical results are verified by an application of the Kuramoto oscillator networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"970-981"},"PeriodicalIF":6.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465711","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 : 2024-12-25DOI: 10.1109/TNSE.2024.3522198
Neeraj Sharma;Kalpesh Kapoor
Blockchain-based cryptocurrencies have grown rapidly over the past decade, but issues with scalability are limiting their wider adoption. Payment Channel Network, a layer two solution, is an alternative for enhancing the scalability of a blockchain network. Two users can engage in some off-chain transactions via payment channels in the network they build in order to avoid the time and expense of on-chain settlement. The number of nodes in the Bitcoin payment channel network has nearly doubled over the last two years, and this network size is proliferating. The number of transactions on the network will increase along with its growth. However, the existing distributed routing algorithms cannot effectively schedule several concurrent transactions due to their static nature. We propose the maxREE algorithm, which efficiently handles concurrent transactions with negligible overhead. Our algorithm considers substituting the necessary edges with superior alternatives to prevent the saturation of a channel's directional capacity while maintaining the height of the underlying routing tree. The transaction flow was enhanced by our proposed algorithm's self-rebalancing and link load sharing. Without compromising network privacy, the unused links are dynamically substituted for the congested ones. We have also developed a simulator, called DRLNsim, to compare our algorithm with existing techniques. On the simulator, the routing approaches are examined using multiple custom network sizes. The proposed method enabled concurrent transactions 58% more effectively on average than existing techniques. The simulation's outcomes mirror the patterns found through theoretical study.
{"title":"maxREE: Maximizing Flow by Replacing Exhausted Edges in Lightning Network","authors":"Neeraj Sharma;Kalpesh Kapoor","doi":"10.1109/TNSE.2024.3522198","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3522198","url":null,"abstract":"Blockchain-based cryptocurrencies have grown rapidly over the past decade, but issues with scalability are limiting their wider adoption. Payment Channel Network, a layer two solution, is an alternative for enhancing the scalability of a blockchain network. Two users can engage in some off-chain transactions via payment channels in the network they build in order to avoid the time and expense of on-chain settlement. The number of nodes in the Bitcoin payment channel network has nearly doubled over the last two years, and this network size is proliferating. The number of transactions on the network will increase along with its growth. However, the existing distributed routing algorithms cannot effectively schedule several concurrent transactions due to their static nature. We propose the <italic>maxREE</i> algorithm, which efficiently handles concurrent transactions with negligible overhead. Our algorithm considers substituting the necessary edges with superior alternatives to prevent the saturation of a channel's directional capacity while maintaining the height of the underlying routing tree. The transaction flow was enhanced by our proposed algorithm's self-rebalancing and link load sharing. Without compromising network privacy, the unused links are dynamically substituted for the congested ones. We have also developed a simulator, called <italic>DRLNsim</i>, to compare our algorithm with existing techniques. On the simulator, the routing approaches are examined using multiple custom network sizes. The proposed method enabled concurrent transactions 58% more effectively on average than existing techniques. The simulation's outcomes mirror the patterns found through theoretical study.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1011-1025"},"PeriodicalIF":6.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465696","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 : 2024-12-25DOI: 10.1109/TNSE.2024.3520967
Fan Wang;Alex Smolyak;Gaogao Dong;Lixin Tian;Shlomo Havlin;Alon Sela
Nodes in complex networks are generally allocated into groups using community detection methods. These communities are based on the interactions between nodes (links). Conversely, in machine learning, clustering methods group data points into classes based on their attribute's similarities regardless of their interactions. Although both communities and clustering methods classify data points into groups, they are fundamentally different. Clustering relies on attribute similarity, while communities focus on interaction patterns. The present study bridges these two distinct approaches by introducing a new concept - Self-Liking Groups (SLG). Based on entropy considerations, SLG quantifies the preference of node classes to interact with similar ones based on their communication patterns, thus combining both the community and the clustering methods. We demonstrate SLG in three case studies: (i) A career network of 2.5 million companies, linked by 8 million job switches. Here, SLG reveals the openness of different industrial sectors to workers in other sectors. For example, the Healthcare sector shows the highest SLG, i.e., it is the least open to accepting workers from other sectors, while the Energy sector has a high SLG, but only for educated workers. Also, managers' shift between different sectors is more limited due to higher SLG. (ii) A scientific co-authorship network where SLG measures the openness of collaboration between different countries. China, India and Japan, have stronger SLG and are thus more likely to collaborate with scientists in their own country compared to the USA, Canada, and most EU countries. (iii) In the medical scientific research space, SLG reveals that Japan, a country known for its longevity, is extremely close compared to China or India. We also find that SLG is a stable measure across various community detection methods and initial parameter spaces. This implies that SLG captures a fundamental property of networks with heterogeneous nodes and is useful in analyzing real complex network scenarios.
{"title":"Self-Liking Group in Networks With Multi-Class Nodes","authors":"Fan Wang;Alex Smolyak;Gaogao Dong;Lixin Tian;Shlomo Havlin;Alon Sela","doi":"10.1109/TNSE.2024.3520967","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3520967","url":null,"abstract":"Nodes in complex networks are generally allocated into groups using community detection methods. These communities are based on the interactions between nodes (links). Conversely, in machine learning, <italic>clustering methods</i> group data points into classes based on their attribute's similarities regardless of their interactions. Although both communities and clustering methods classify data points into groups, they are fundamentally different. Clustering relies on attribute similarity, while communities focus on interaction patterns. The present study bridges these two distinct approaches by introducing a new concept - Self-Liking Groups (SLG). Based on entropy considerations, SLG quantifies the preference of node classes to interact with similar ones based on their communication patterns, thus combining both the community and the clustering methods. We demonstrate SLG in three case studies: (i) A career network of 2.5 million companies, linked by 8 million job switches. Here, SLG reveals the openness of different industrial sectors to workers in other sectors. For example, the Healthcare sector shows the highest SLG, i.e., it is the least open to accepting workers from other sectors, while the Energy sector has a high SLG, but only for educated workers. Also, managers' shift between different sectors is more limited due to higher SLG. (ii) A scientific co-authorship network where SLG measures the openness of collaboration between different countries. China, India and Japan, have stronger SLG and are thus more likely to collaborate with scientists in their own country compared to the USA, Canada, and most EU countries. (iii) In the medical scientific research space, SLG reveals that Japan, a country known for its longevity, is extremely close compared to China or India. We also find that SLG is a stable measure across various community detection methods and initial parameter spaces. This implies that SLG captures a fundamental property of networks with heterogeneous nodes and is useful in analyzing real complex network scenarios.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"888-899"},"PeriodicalIF":6.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465753","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 : 2024-12-25DOI: 10.1109/TNSE.2024.3519802
Souradeep Das;Riya Tapwal;Sudip Misra
Blockchain is highly dependent on network topology and routing paths for efficient operation. The speed and effectiveness of mining are impacted by network topology, as a well-defined routing path improves the speed of mining by the faster exchange of data for verification. However, high traffic on the path, caused by the exchange of messages among the miners leads to delays in the routing path and lengthens the time needed for mining data. Moreover, the dynamic addition and termination of miners from the network affect the topology, further contributing to delays in the mining process and transactions. To address these challenges, we present a novel system called FlexRout that dynamically predicts the optimal routing path to increase the speed of the mining process. It firstly predicts miners (nodes) who can leave mining by checking their resource availability. Simultaneously, it also grants a reward or a penalty depending upon the right or wrong prediction made respectively. Experimental results demonstrate that FlexRout can significantly reduce traffic by almost 49% and latency by 50%.
{"title":"FlexRout: Dynamic Routing in Blockchain Networks","authors":"Souradeep Das;Riya Tapwal;Sudip Misra","doi":"10.1109/TNSE.2024.3519802","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3519802","url":null,"abstract":"Blockchain is highly dependent on network topology and routing paths for efficient operation. The speed and effectiveness of mining are impacted by network topology, as a well-defined routing path improves the speed of mining by the faster exchange of data for verification. However, high traffic on the path, caused by the exchange of messages among the miners leads to delays in the routing path and lengthens the time needed for mining data. Moreover, the dynamic addition and termination of miners from the network affect the topology, further contributing to delays in the mining process and transactions. To address these challenges, we present a novel system called <italic>FlexRout</i> that dynamically predicts the optimal routing path to increase the speed of the mining process. It firstly predicts miners (nodes) who can leave mining by checking their resource availability. Simultaneously, it also grants a reward or a penalty depending upon the right or wrong prediction made respectively. Experimental results demonstrate that <italic>FlexRout</i> can significantly reduce traffic by almost 49% and latency by 50%.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"838-847"},"PeriodicalIF":6.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465773","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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