IEEE Transactions on Network Science and Engineering最新文献_第10页

A Quantum Safe Mutual Authentication Protocol for Smart Meter Communications With Experimental Evaluation 用于智能电表通信的量子安全相互验证协议及实验评估

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-16 DOI: 10.1109/TNSE.2024.3427110

Rohini Poolat Parameswarath;Chao Wang;Biplab Sikdar

{"title":"A Quantum Safe Mutual Authentication Protocol for Smart Meter Communications With Experimental Evaluation","authors":"Rohini Poolat Parameswarath;Chao Wang;Biplab Sikdar","doi":"10.1109/TNSE.2024.3427110","DOIUrl":"10.1109/TNSE.2024.3427110","url":null,"abstract":"The security landscape will change dramatically with the advent of quantum computers and existing security schemes in various domains including smart grid communications must be updated to make them secure from quantum computer-enabled attacks. In this paper, we propose a quantum-safe mutual authentication protocol, leveraging the concepts of Quantum Key Distribution (QKD) and Quantum Random Number Generator (QRNG), for secure communication between smart meters and a server. Unlike conventional schemes based on cryptographic algorithms that rely on difficulties to solve certain mathematical problems, the proposed protocol is secure against attacks arising from quantum computers. In the proposed protocol, QKD is employed to establish secure keys in smart meter communications with provable security while QRNG provides truly random numbers that are unknown to any eavesdropper. Specifically, we employ the Measurement-Device-Independent Quantum Key Distribution (MDI QKD), a type of QKD whose security does not rely on any assumptions about measurement devices. We provide a formal security proof for the proposed scheme under the real-or-random (RoR) model. Additionally, we conduct a proof-of-concept experimental demonstration, using the secure keys from a MDI QKD system and random numbers from QRNG, to demonstrate the feasibility and practicality of the proposed scheme.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"5058-5072"},"PeriodicalIF":6.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721551","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}

引用次数: 0

Enable Microgrid Energy Management: A Graph Based Aggregative Game Approach 实现微电网能源管理：基于图的聚合博弈方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-16 DOI: 10.1109/TNSE.2024.3429393

Gehui Xu;Yaoyu Zhang;Jian Sun;Guanpu Chen;Chenye Wu

引用次数: 0

Opportunistic Sensing in Task-Oriented Wireless Sensor Network Based on Graph Compressed Sensing 基于图压缩传感的任务导向型无线传感器网络中的机会传感

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-16 DOI: 10.1109/TNSE.2024.3427129

Wei Wang;Hefei Gao;Lei Xu

引用次数: 0

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-Term Load Forecasting in Electricity Wholesale Markets 电力批发市场中用于稳健短期负荷预测的深度强化学习辅助联合学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-15 DOI: 10.1109/TNSE.2024.3427672

Chenghao Huang;Shengrong Bu;Weilong Chen;Hao Wang;Yanru Zhang

{"title":"Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-Term Load Forecasting in Electricity Wholesale Markets","authors":"Chenghao Huang;Shengrong Bu;Weilong Chen;Hao Wang;Yanru Zhang","doi":"10.1109/TNSE.2024.3427672","DOIUrl":"10.1109/TNSE.2024.3427672","url":null,"abstract":"Short-term load forecasting (STLF) plays a pivotal role in operational efficiency of power plants. Leveraging data from utility companies for STLF in a wholesale market presents challenges. Notably, data sharing reluctance from utility companies, driven by privacy considerations, limits the availability of valuable forecasting information. Concurrently, due to the growing reliance on information and communication technologies, data integrity attacks (DIAs) and communication noise are emerging as a significant concern, which is largely overlooked in existing research. We propose an innovative approach combining deep reinforcement learning (DRL) with federated learning (FL) to construct a robust STLF model that meets privacy constraints and operates efficiently. By employing FL, we facilitate collaboration between the power plant and multiple utility companies to generate a STLF model for the power plant, circumventing the need for direct access to raw data from utility companies, thereby preserving data privacy. To counteract model degradation induced by DIAs and noise in communication channels, we incorporate DRL into our methodology. Simulation outcomes affirm the efficacy of our proposed approach, demonstrating its capacity to deliver accurate and resilient STLF for power plants, even in the presence of DIAs and communication noise.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"5073-5086"},"PeriodicalIF":6.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141722410","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}

引用次数: 0

Node Allocation Strategy for Low Latency Neighborhood Area Networks in Smart Grid 智能电网中低延迟邻域网络的节点分配策略

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-15 DOI: 10.1109/TNSE.2024.3427840

Bhargavi Goswami;Raja Jurdak;Ghavameddin Nourbakhsh

{"title":"Node Allocation Strategy for Low Latency Neighborhood Area Networks in Smart Grid","authors":"Bhargavi Goswami;Raja Jurdak;Ghavameddin Nourbakhsh","doi":"10.1109/TNSE.2024.3427840","DOIUrl":"10.1109/TNSE.2024.3427840","url":null,"abstract":"Neighborhood area networks (NANs) lay the foundation for robust communication in smart grids to support stable and secure end-user connectivity with substations. Firstly, the current solutions are unrealistic to meet the time-bound requirements for smart grid applications with large number of intermediate node connectivity in NANs. Secondly, the existing Low-power Wireless Personal Area Network (LoWPAN) does not scale up to a thousand nodes while meeting the latency requirement of delay-critical smart grid applications. To address both the scaling and latency issues, this paper proposes the use of long-range communication, such as 5G, to complement short-range communication in NANs that effectively create a two-layer, primary and secondary networks. We identified the need for a Node Allocation Strategy (NAS) to reduce the latency for scaling NANs. NAS, particularly, is designed for NANs with thousands of smart meter nodes located at secondary networks. We model the NAS to reduce the hop count between smart meters and the utility center through an algorithm designed and proposed in this paper. The proposed NAS communication strategy for NANs applied to various dense and sparsely populated real smart grid scenarios to examine the efficacy of this approach.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"5087-5098"},"PeriodicalIF":6.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721552","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}

引用次数: 0

Optimal EV Charging Decisions Considering Charging Rate Characteristics and Congestion Effects 考虑充电率特征和拥堵效应的电动汽车充电最优决策

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-08 DOI: 10.1109/TNSE.2024.3424443

Lihui Yi;Ermin Wei

{"title":"Optimal EV Charging Decisions Considering Charging Rate Characteristics and Congestion Effects","authors":"Lihui Yi;Ermin Wei","doi":"10.1109/TNSE.2024.3424443","DOIUrl":"10.1109/TNSE.2024.3424443","url":null,"abstract":"With the rapid growth in demand for electric vehicles (EVs), corresponding charging infrastructures are expanding. These charging stations are located at various places with different congestion levels. EV drivers face an important decision in choosing between charging stations to reduce their overall time costs. However, existing literature either assumes a flat charging rate and hence overlooks the physical characteristics of an EV battery where charging rate is typically reduced as the battery charges, or ignores the effect of other drivers on an EV's decision making process. In this paper, we consider both the predetermined exogenous wait cost and the endogenous congestion induced by other drivers' strategic decisions, and propose a differential equation based approach to find the optimal strategies. We analytically characterize the equilibrium strategies and find that co-located EVs may make different decisions depending on the charging rate and/or remaining battery levels. Through numerical experiments, we investigate the impact of charging rate characteristics, modeling parameters and the consideration of endogenous congestion levels on the optimal charging decisions. Finally, we apply real-world data and find that some EV users with slower charging rates may benefit from the participation of fast-charging EVs.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"5045-5057"},"PeriodicalIF":6.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141569037","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}

引用次数: 0

Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization 利用 K 切排序和 Krill-herd 优化在复杂加权网络中进行分区聚类

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-04 DOI: 10.1109/TNSE.2024.3423418

Vishal Srivastava;Shashank Sheshar Singh;Ankush Jain

{"title":"Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization","authors":"Vishal Srivastava;Shashank Sheshar Singh;Ankush Jain","doi":"10.1109/TNSE.2024.3423418","DOIUrl":"10.1109/TNSE.2024.3423418","url":null,"abstract":"Network partitioning has been studied extensively on undirected and weighted networks that need to partition the graph into small clusters. Graph-cutting is a widely known approach that removes the inter-cluster edges to find the local network clusters. Cutting a network into small clusters is pivotal in a mixed integer optimization problem. Proper selection of cut sequences discards the possibility of trivial partitions and reduces the computation load to improve cluster quality. Proper cut-sequence selection relies on multiple heuristics that restrict this problem from being generalized. Cut-sequence selection is an NP-hard problem that turns out to be challenging for weighted networks. This paper presents a swarm-heuristics-based framework to solve the cut-sequence selection problem in weighted networks. First, we generate an affinity network from a given data set. A cost-based objective function is formalized that takes cut sequences as input and returns the weighted intra-cluster connected components. Subsequently, heuristics-based cut sequences are initialized, and krill-herd optimization is used to solve the objective function. The framework is empirically tested on simulated and real-world networks. Network-based indices are used to measure the quality of partitions. The comparative analysis, computation time, and convergence analysis are performed with state-of-the-art methods to report the competitive behavior of the framework. The framework is highly effective and has paved new ways for future research to solve the cut-sequence selection problem without prior knowledge.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"5035-5044"},"PeriodicalIF":6.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550447","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}

引用次数: 0

Distributed Design of Strong Structurally Controllable and Maximally Robust Networks 强结构可控和最大鲁棒性网络的分布式设计

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-04 DOI: 10.1109/TNSE.2024.3418992

Priyanshkumar I. Patel;Johir Suresh;Waseem Abbas

{"title":"Distributed Design of Strong Structurally Controllable and Maximally Robust Networks","authors":"Priyanshkumar I. Patel;Johir Suresh;Waseem Abbas","doi":"10.1109/TNSE.2024.3418992","DOIUrl":"10.1109/TNSE.2024.3418992","url":null,"abstract":"This paper studies the problem of designing multiagent networks that simultaneously achieve strong structural controllability (SSC) and maximal robustness. Though crucial for effective operation, these two properties can conflict in multiagent systems. We present novel methods to construct network graphs that balance these competing requirements while accounting for network parameters such as the total number of agents \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 and the number of leaders \u0000<inline-formula><tex-math>$N_{L}$</tex-math></inline-formula>\u0000 (agents utilized to inject external inputs into the network). We further extend our framework to incorporate the network diameter \u0000<inline-formula><tex-math>$D$</tex-math></inline-formula>\u0000, thereby generating both maximally robust and strong structurally controllable networks for given parameters \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$N_{L}$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$D$</tex-math></inline-formula>\u0000. To assess controllability, we employ the notion of zero forcing sets in graphs and rigorously evaluate the robustness of our designs. We also present a distributed approach to constructing these networks, leveraging graph grammars. This work explores the trade-off between network controllability and robustness to achieve multiple design objectives in multiagent systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"4428-4442"},"PeriodicalIF":6.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550446","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}

引用次数: 0

Energy Consumption Minimization for Hybrid Federated Learning and Offloadable Tasks in UAV-Enabled WPCN 无人机支持的 WPCN 中混合联合学习和可卸载任务的能耗最小化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-03 DOI: 10.1109/TNSE.2024.3422658

Qiang Tang;Yong Yang;Halvin Yang;Dun Cao;Kun Yang

{"title":"Energy Consumption Minimization for Hybrid Federated Learning and Offloadable Tasks in UAV-Enabled WPCN","authors":"Qiang Tang;Yong Yang;Halvin Yang;Dun Cao;Kun Yang","doi":"10.1109/TNSE.2024.3422658","DOIUrl":"10.1109/TNSE.2024.3422658","url":null,"abstract":"In recent years, federated learning (FL) has been adopted in mobile edge computing (MEC) to protect user privacy. However, in some cases, in addition to performing FL to process private data, users also need to process other non-private data. Therefore, how to integrate private data and non-private data in a MEC system for comprehensive processing is an issue worth studying. In this paper, we propose an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN) to process both FL tasks and offloadable MEC tasks of UEs, where the UAV charges UEs via wireless power transfer (WPT) technology, executes offloaded tasks, and aggregates the FL model parameters. To minimize energy consumption of the UAV, we formulate a problem to jointly optimize the hovering position of UAV, the WPT power, the proportion of UAV's computing resources, the percentage of offloaded tasks, and the time scheduling of FL under the energy harvesting constraint. The problem is divided into three subproblems with the aid of block coordinate descent (BCD) method. These subproblems are solved by Lagrange method and heuristic algorithm respectively. Numerical results show our algorithm can reduce the energy consumption of the UAV with low time complexity compared with several benchmarks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"4639-4650"},"PeriodicalIF":6.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550449","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}

引用次数: 0

VNEavXT: Offline Virtual Network Embedding Model Considering Crosstalk-Avoided Approach in Spectrally-Spatially Elastic Optical Networks VNEavXT：考虑频谱空间弹性光网络中的串扰规避方法的离线虚拟网络嵌入模型

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-03 DOI: 10.1109/TNSE.2024.3421246

Vinay Kumar;Joy Halder;Abhijit Mitra;Eiji Oki;Bijoy Chand Chatterjee

{"title":"VNEavXT: Offline Virtual Network Embedding Model Considering Crosstalk-Avoided Approach in Spectrally-Spatially Elastic Optical Networks","authors":"Vinay Kumar;Joy Halder;Abhijit Mitra;Eiji Oki;Bijoy Chand Chatterjee","doi":"10.1109/TNSE.2024.3421246","DOIUrl":"10.1109/TNSE.2024.3421246","url":null,"abstract":"Infrastructure as a service and network virtualization facilitates the network provider/operator to lease out the resources among different stakeholders, hence promoting the sharing of network resources among stakeholders. At the same time, spectrally-spatially elastic optical networks (SS-EONs) have emerged as a prominent solution to release pressures out of the backhaul network by overcoming the physical barrier and expanding it in the spatial dimension. The efficient mapping of virtual optical network (VON) requests is challenging in SS-EONs because of the additional constraints. For the first time, this work proposes a crosstalk (XT)-avoided model for routing, spectrum, core, and mode allocation (RSCMA), named VNEavXT, to improve resource utilization by avoiding inter-core and inter-mode XT. VNEavXT allocates the network resources fulfilling the spectrum continuity, spectrum contiguity, core continuity, mode continuity, and XT-avoided constraints. An integer linear programming (ILP) problem is formulated for VNEavXT. We prove that the decision version of VNEavXT is NP-complete. Two heuristic algorithms are introduced for large instances where the ILP approach becomes intractable. Finally, their performances are evaluated by considering different performance metrics. We observe that the heuristic based on ILP embeds more requests than the heuristic based on rank and a benchmark heuristic designed for static (off-line) virtual optical network mapping, but in terms of computation time, the heuristic based on rank is faster than the heuristic based on ILP and the benchmark.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"4807-4821"},"PeriodicalIF":6.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550448","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}

引用次数: 0