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2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献

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Poster: Towards Efficient Multilayer Collaboration for CAV Applications 海报:面向CAV应用的高效多层协作
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00040
Sidi Lu, Weisong Shi
{"title":"Poster: Towards Efficient Multilayer Collaboration for CAV Applications","authors":"Sidi Lu, Weisong Shi","doi":"10.1109/SEC54971.2022.00040","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00040","url":null,"abstract":"Connected and autonomous vehicles (CAV s) are facing increasing amounts of data and more complex data analysis, which creates challenges for them to make reliable decisions in real-time. To enable time-sensitive CAV applications, we design and implement a vehicle-edge-cloud framework that integrates compressed imaging (CI) and edge computing into CAV systems. Specifically, a lightweight model is used on the vehicle to perform real-time detection based on optical domain compressed data (called measurements). The edge is responsible for receiving the measurements and performing video reconstruction to support (more accurate) analysis based on the reconstructed video with a trigger. At the same time, the measurements, reconstructed videos, and analysis results are sent to the cloud to continuously update the vehicle model. In addition, we apply reinforcement learning to adapt the compression rate in different driving scenarios. The proposed framework is fully evaluated using our designed roadside platform and outdoor delivery vehicles.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129427275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Diagnosing Clinical Diseases using an Edge-Enabled Deep Learning Technology 使用边缘支持深度学习技术诊断临床疾病
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00080
Kangjun Bai, Y. Yi
{"title":"Diagnosing Clinical Diseases using an Edge-Enabled Deep Learning Technology","authors":"Kangjun Bai, Y. Yi","doi":"10.1109/SEC54971.2022.00080","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00080","url":null,"abstract":"Along with the development of high-speed communication networks, edge-enabled mobile devices have opened new possibilities for diagnosing health conditions or developing suitable treatment plans. While the latest deep learning technology has deployed to restructure and translate complex medical applications, the costly training operation using large-scale neural networks with tremendous amount of data remain the major challenge. In this work, we take advantages of reservoir computing to develop a reliable and low-cost medical diagnostic system for edge-enabled devices. Specifically, an echo state network (ESN) was trained to discover non-obvious correlation and likelihood from biomedical data with respect to various patients. Through the determination of cardiovascular and coronavirus diseases, numerical evaluations demonstrated advantage of ESN against the state-of-the-art. At particularly no computation overhead, ESN precisely described the prediction tasks of health conditions, offering improvements of up to 1000x in sample reduction, 175x in training speedup, and 15 percentage points in prediction accuracy.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115267431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Demo: Nearby Aggressive Driving Detection 演示:附近侵略性驾驶检测
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00030
Tomohiro Matsuda, Seyhan Uçar, Yongkang Liu, E. A. Sisbot, K. Oguchi
{"title":"Demo: Nearby Aggressive Driving Detection","authors":"Tomohiro Matsuda, Seyhan Uçar, Yongkang Liu, E. A. Sisbot, K. Oguchi","doi":"10.1109/SEC54971.2022.00030","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00030","url":null,"abstract":"Aggressive driving is the leading cause of many fatal crashes. Ego vehicles should detect such dangerous driving behavior on other cars and guide drivers to mitigate collision risk. In this paper, we focus on that use case. We demonstrate a nearby aggressive driving detection system. In nearby aggressive driving detection, the ego vehicle observes the follower vehicle and detects aggressive driving behavior on the follower vehicle. It notifies its driver whenever the follower vehicle exhibits aggressive driving.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115111974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantum Persistent Homology for Time Series 时间序列的量子持久同调
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-11-08 DOI: 10.1109/SEC54971.2022.00057
Bernardo Ameneyro, G. Siopsis, V. Maroulas
{"title":"Quantum Persistent Homology for Time Series","authors":"Bernardo Ameneyro, G. Siopsis, V. Maroulas","doi":"10.1109/SEC54971.2022.00057","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00057","url":null,"abstract":"Persistent homology, a powerful mathematical tool for data analysis, summarizes the shape of data through tracking topological features across changes in different scales. Classical algorithms for persistent homol-ogy are often constrained by running times and mem-ory requirements that grow exponentially on the number of data points. To surpass this problem, two quantum algorithms of persistent homology have been developed based on two different approaches. However, both of these quantum algorithms consider a data set in the form of a point cloud, which can be restrictive considering that many data sets come in the form of time series. In this paper, we alleviate this issue by establishing a quantum Takens's delay embedding algorithm, which turns a time series into a point cloud by considering a pertinent embedding into a higher dimensional space. Having this quantum transformation of time series to point clouds, then one may use a quantum persistent homology algorithm to extract the topological features from the point cloud associated with the original times series.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133030086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Machine Learning based Discrimination for Excited State Promoted Readout 基于机器学习的激发态提升读出判别
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-10-16 DOI: 10.1109/SEC54971.2022.00053
Utkarsh Azad, Helena Zhang
{"title":"Machine Learning based Discrimination for Excited State Promoted Readout","authors":"Utkarsh Azad, Helena Zhang","doi":"10.1109/SEC54971.2022.00053","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00053","url":null,"abstract":"A limiting factor for readout fidelity for superconducting qubits is the relaxation of the qubit to the ground state before the time needed for the resonator to reach its final target state. A technique known as excited state promoted (ESP) readout was proposed to reduce this effect and further improve the readout contrast on superconducting hardware. In this work, we use readout data from five-qubit IBMQ devices to measure the effectiveness of using deep neural networks, like feedforward neural networks, and various classification algorithms, like k-nearest neighbors, decision trees, and Gaussian naive Bayes, for single-qubit and multi-qubit discrimination. These methods were compared to standardly used linear and quadratic discriminant analysis algorithms based on their qubit-state-assignment fidelity performance, robustness to readout crosstalk, and training time.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130387867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
ENTS: An Edge-native Task Scheduling System for Collaborative Edge Computing 基于协同边缘计算的边缘本地任务调度系统
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-10-14 DOI: 10.1109/SEC54971.2022.00019
Mingjin Zhang, Jiannong Cao, Lei Yang, L. Zhang, Yuvraj Sahni, Shan Jiang
{"title":"ENTS: An Edge-native Task Scheduling System for Collaborative Edge Computing","authors":"Mingjin Zhang, Jiannong Cao, Lei Yang, L. Zhang, Yuvraj Sahni, Shan Jiang","doi":"10.1109/SEC54971.2022.00019","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00019","url":null,"abstract":"Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge nodes. Recently, there has been a trend to orchestrate and schedule containerized application workloads in CEC, while Kubernetes has become the de-facto standard broadly adopted by the industry and academia. However, Kubernetes is not preferable for CEC because its design is not dedicated to edge computing and neglects the unique features of edge nativeness. More specifically, Kubernetes primarily ensures resource provision of workloads while neglecting the performance requirements of edge-native applications, such as throughput and latency. Furthermore, Kubernetes neglects the inner dependencies of edge-native applications and fails to consider data locality and networking resources, leading to inferior performance. In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status. We showcase the superior efficacy of ENTS with a case study on data streaming applications. We mathematically formulate a joint task allocation and flow scheduling problem that maximizes the job throughput. We design two novel online scheduling algorithms to optimally decide the task allocation, bandwidth allocation, and flow routing policies. The extensive experiments on a real-world edge video analytics application show that ENTS achieves 43% -220% higher average job throughput compared with the state-of-the-art.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131255821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning 二叉树上量子行走的高效电路实现及其在强化学习中的应用
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-10-13 DOI: 10.1109/SEC54971.2022.00066
Thomas Mullor, David Vigouroux, Louis Béthune
{"title":"Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning","authors":"Thomas Mullor, David Vigouroux, Louis Béthune","doi":"10.1109/SEC54971.2022.00066","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00066","url":null,"abstract":"Quantum walks on binary trees are used in many quantum algorithms to achieve important speedup over classical algorithms. The formulation of this kind of algorithms as quantum circuit presents the advantage of being easily readable, executable on circuit based quantum computers and simulators and optimal on the usage of resources. We propose a strategy to compose quantum circuit that performs quantum walk on binary trees following universal gate model quantum computation principles. We give a particular attention to NAND formula evaluation algorithm as it could have many applications in game theory and reinforcement learning. We therefore propose an application of this algorithm and show how it can be used to train a quantum reinforcement learning agent in a two player game environment.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124792671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation 量子卷积神经网络:基于纠缠反向传播的量子卷积神经网络
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-10-11 DOI: 10.1109/SEC54971.2022.00054
S. Stein, Y. Mao, James Ang, A. Li
{"title":"QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation","authors":"S. Stein, Y. Mao, James Ang, A. Li","doi":"10.1109/SEC54971.2022.00054","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00054","url":null,"abstract":"Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend, and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the backpropagated gradients, and training a filter state against an ideal target state.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133572818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Time Minimization in Hierarchical Federated Learning 分层联邦学习中的时间最小化
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-10-07 DOI: 10.1109/SEC54971.2022.00015
Chang Liu, Terence Jie Chua, Junfeng Zhao
{"title":"Time Minimization in Hierarchical Federated Learning","authors":"Chang Liu, Terence Jie Chua, Junfeng Zhao","doi":"10.1109/SEC54971.2022.00015","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00015","url":null,"abstract":"Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132251895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
On Quantum-Enhanced LDPC Decoding for Rayleigh Fading Channels 瑞利衰落信道的量子增强LDPC解码研究
2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-09-24 DOI: 10.1109/SEC54971.2022.00070
Utso Majumder, Aditya Das Sarma, Vishnu Vaidya, M. Chandra
{"title":"On Quantum-Enhanced LDPC Decoding for Rayleigh Fading Channels","authors":"Utso Majumder, Aditya Das Sarma, Vishnu Vaidya, M. Chandra","doi":"10.1109/SEC54971.2022.00070","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00070","url":null,"abstract":"Quantum and Classical computers continue to work together in tight cooperation to solve difficult problems. The combination is thus suggested in recent times for decoding the Low Density Parity Check (LDPC) codes, for the next generation Wireless Communication systems. In this paper we have worked out the Quadratic Unconstrained Binary Optimization (QUBO) formulation for Rayleigh Fading channels for two different scenarios- channel state fully known and not known. The resultant QUBO are solved using D-Wave 2000Q Quantum Annealer and the outputs from the Annealer are classically postprocessed, invoking the notion of diversity. Simple minimum distance decoding of the available copies of the outputs led to improved performance, compared to picking the minimum-energy solution in terms of Bit Error Rate (BER). Apart from providing these results and the comparisons to fully classical Simulated Annealing (SA) and the traditional Belief Propagation (BP) based strategies, some remarks about diversity due to quantum processing are also spelt out.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126837572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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