IEEE Transactions on Sustainable Computing最新文献_第5页

Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach 具有量化效应的电力谐波的动态事件触发状态估计：区位集合成员方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-19 DOI: 10.1109/TSUSC.2024.3391733

Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao

{"title":"Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach","authors":"Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao","doi":"10.1109/TSUSC.2024.3391733","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3391733","url":null,"abstract":"This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of: 1) unknown but bounded noises; 2) quantization effects; and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the \u0000<inline-formula><tex-math>$F$</tex-math></inline-formula>\u0000-radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"803-813"},"PeriodicalIF":3.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Adaptive Mobile Chargers Scheduling Scheme Based on AHP-MCDM for WRSN 基于 AHP-MCDM 的 WRSN 自适应移动充电器调度方案

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-19 DOI: 10.1109/TSUSC.2024.3391316

Kondwani Makanda;Ammar Hawbani;Xingfu Wang;Abdulbary Naji;Ahmed Al-Dubai;Liang Zhao;Saeed Hamood Alsamhi

{"title":"Adaptive Mobile Chargers Scheduling Scheme Based on AHP-MCDM for WRSN","authors":"Kondwani Makanda;Ammar Hawbani;Xingfu Wang;Abdulbary Naji;Ahmed Al-Dubai;Liang Zhao;Saeed Hamood Alsamhi","doi":"10.1109/TSUSC.2024.3391316","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3391316","url":null,"abstract":"Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"57-69"},"PeriodicalIF":3.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Memristive Clustering: A Novel Sustainable Parameter Selection Based on Memristive Circuit Model 忆忆聚类：一种基于忆忆电路模型的可持续参数选择方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-18 DOI: 10.1109/TSUSC.2024.3387727

Kaikai Qiao;Ben Ma;Lidan Wang;Shukai Duan

引用次数: 0

Network Anomaly Detection With Stacked Sparse Shrink Variational Autoencoders and Unbalanced XGBoost 基于堆叠稀疏收缩变分自编码器和不平衡XGBoost的网络异常检测

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-16 DOI: 10.1109/TSUSC.2024.3390003

Jing Bi;Ziyue Guan;Haitao Yuan;Jinhong Yang;Jia Zhang

{"title":"Network Anomaly Detection With Stacked Sparse Shrink Variational Autoencoders and Unbalanced XGBoost","authors":"Jing Bi;Ziyue Guan;Haitao Yuan;Jinhong Yang;Jia Zhang","doi":"10.1109/TSUSC.2024.3390003","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3390003","url":null,"abstract":"Efficient and accurate identification of network anomalies is significant to network security systems. It is highly challenging to detect abnormal behaviors in the increasing network data accurately. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have unsatisfying detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. In this letter, based on the improvement of variational autoencoders, stacked sparse shrink variational autoencoders (S3VAEs) are designed. In addition, an <underline>U</u>nbalanced <underline>X</u>GBoost classifier based on <underline>G</u>enetic simulated annealing particle swarm optimization (UXG) is proposed. Finally, the feature extractor of S3VAEs is combined with the UXG classifier, and the anomaly detection model is obtained. Experimental results based on four real-life data sets demonstrate that the proposed anomaly detection model achieves higher classification accuracy and F1 than several state-of-the-art algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"28-38"},"PeriodicalIF":3.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Staged Noise Perturbation for Privacy-Preserving Federated Learning 基于阶段噪声摄动的隐私保护联邦学习

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-04 DOI: 10.1109/TSUSC.2024.3381812

Zhe Li;Honglong Chen;Yudong Gao;Zhichen Ni;Huansheng Xue;Huajie Shao

{"title":"Staged Noise Perturbation for Privacy-Preserving Federated Learning","authors":"Zhe Li;Honglong Chen;Yudong Gao;Zhichen Ni;Huansheng Xue;Huajie Shao","doi":"10.1109/TSUSC.2024.3381812","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3381812","url":null,"abstract":"Federated learning (FL) is a distributed machine learning paradigm that addresses the challenges of privacy leakage and data silos by collaboratively training the global model through parameter exchange, rather than data, between the central server and local clients. However, recent researches highlight the vulnerability of FL to gradient leakage attacks where adversaries exploit shared parameters from clients to reconstruct sensitive training data. Differential privacy (DP) effectively mitigates this threat by adding noise to shared parameters, yet introduces a trade-off between privacy and accuracy in FL. To better balance the privacy and accuracy, in this paper we propose a staged noise perturbation strategy, called alternating noise permutation (ANP), from a novel perspective. ANP adds Gaussian-distributed random noise to model parameters during the critical learning period of FL, following DP principles. While in non-critical learning period, ANP alternately permutes the noise during odd and even communication rounds, achieving near mutual cancellation and mitigating the negative impact. Experimental results across three datasets and two neural networks under both independent identical distribution (IID) and NonIID scenarios demonstrate that ANP significantly improves classification accuracy and exhibits robustness against gradient leakage attack, ensuring the effectiveness of FL for secure and accurate collaborative model training.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"936-947"},"PeriodicalIF":3.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

2024 Reviewers List 2024 年审稿人名单

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-03 DOI: 10.1109/TSUSC.2024.3353082

引用次数: 0

APPQ-CNN: An Adaptive CNNs Inference Accelerator for Synergistically Exploiting Pruning and Quantization Based on FPGA 基于FPGA的协同利用修剪和量化的自适应cnn推理加速器APPQ-CNN

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-03-27 DOI: 10.1109/TSUSC.2024.3382157

Xian Zhang;Guoqing Xiao;Mingxing Duan;Yuedan Chen;Kenli Li

{"title":"APPQ-CNN: An Adaptive CNNs Inference Accelerator for Synergistically Exploiting Pruning and Quantization Based on FPGA","authors":"Xian Zhang;Guoqing Xiao;Mingxing Duan;Yuedan Chen;Kenli Li","doi":"10.1109/TSUSC.2024.3382157","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3382157","url":null,"abstract":"Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing applications such as computational vision and image processing. However, as the number of layers of the CNN model increases, the number of parameters and computations gets larger, making it increasingly challenging to accelerate in edge computing applications. To effectively adapt to the tradeoff between the speed and accuracy of CNNs inference for smart applications. This paper proposes an FPGA-based adaptive CNNs inference accelerator synergistically utilizing filter pruning, fixed-point parameter quantization, and multi-computing unit parallelism called APPQ-CNN. First, the article devises a hybrid pruning algorithm based on the L1-norm and APoZ to measure the filter impact degree and a configurable parameter quantization fixed-point computing architecture instead of floating-point architecture. Then, design a cascade of the CNN pipelined kernel architecture and configurable multiple computation units. Finally, conduct extensive performance exploration and comparison experiments on various real and synthetic datasets. With negligible accuracy loss, the speed performance of our accelerator APPQ-CNN compares with current state-of-the-art FPGA-based accelerators PipeCNN and OctCNN by 2.15× and 1.91×, respectively. Furthermore, APPQ-CNN provides settable fixed-point quantization bit-width parameters, filter pruning rate, and multiple computation unit counts to cope with practical application performance requirements in edge computing.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"874-888"},"PeriodicalIF":3.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Deadline-Aware Cost and Energy Efficient Offloading in Mobile Edge Computing 移动边缘计算中的截止时间感知成本与能效卸载

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-03-26 DOI: 10.1109/TSUSC.2024.3381841

Mohit Kumar;Avadh Kishor;Pramod Kumar Singh;Kalka Dubey

{"title":"Deadline-Aware Cost and Energy Efficient Offloading in Mobile Edge Computing","authors":"Mohit Kumar;Avadh Kishor;Pramod Kumar Singh;Kalka Dubey","doi":"10.1109/TSUSC.2024.3381841","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3381841","url":null,"abstract":"The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"778-789"},"PeriodicalIF":3.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review 无线电力传输技术、应用和未来趋势：综述

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-03-22 DOI: 10.1109/TSUSC.2024.3380607

Aisha Alabsi;Ammar Hawbani;Xingfu Wang;Ahmed Al-Dubai;Jiankun Hu;Samah Abdel Aziz;Santosh Kumar;Liang Zhao;Alexey V. Shvetsov;Saeed Hamood Alsamhi

{"title":"Wireless Power Transfer Technologies, Applications, and Future Trends: A Review","authors":"Aisha Alabsi;Ammar Hawbani;Xingfu Wang;Ahmed Al-Dubai;Jiankun Hu;Samah Abdel Aziz;Santosh Kumar;Liang Zhao;Alexey V. Shvetsov;Saeed Hamood Alsamhi","doi":"10.1109/TSUSC.2024.3380607","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3380607","url":null,"abstract":"Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"1-17"},"PeriodicalIF":3.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

FedGCN: A Federated Graph Convolutional Network for Privacy-Preserving Traffic Prediction 基于联邦图卷积网络的保密性交通预测

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-03-22 DOI: 10.1109/TSUSC.2024.3395350

Na Hu;Wei Liang;Dafang Zhang;Kun Xie;Kuanching Li;Albert Y. Zomaya

{"title":"FedGCN: A Federated Graph Convolutional Network for Privacy-Preserving Traffic Prediction","authors":"Na Hu;Wei Liang;Dafang Zhang;Kun Xie;Kuanching Li;Albert Y. Zomaya","doi":"10.1109/TSUSC.2024.3395350","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3395350","url":null,"abstract":"Traffic prediction is crucial for intelligent transportation systems, assisting in making travel decisions, minimizing traffic congestion, and improving traffic operation efficiency. Although effective, existing centralized traffic prediction methods have privacy leakage risks. Federated learning-based traffic prediction methods keep raw data local and train the global model in a distributed way, thus preserving data privacy. Nevertheless, the spatial correlations between local clients will be broken as data exchange between local clients is not allowed in federated learning, leading to missing spatial information and inferior prediction accuracy. To this end, we propose a federated graph neural network with spatial information completion (FedGCN) for privacy-preserving traffic prediction by adopting a federated learning scheme to protect confidentiality and presenting a mending graph convolutional neural network to mend the missing spatial information during capturing spatial dependency to improve prediction accuracy. To complete the missing spatial information efficiently and capture the client-specific spatial pattern, we design a personalized training scheme for the mending graph neural network, reducing communication overhead. The experiments on four public traffic datasets demonstrate that the proposed model outperforms the best baseline with a ratio of 3.82%, 1.82%, 2.13%, and 1.49% in terms of absolute mean error while preserving privacy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"925-935"},"PeriodicalIF":3.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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