2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献_第6页

Poster: SlideCNN: Deep Learning for Auditory Spatial Scenes with Limited Annotated Data 基于有限注释数据的听觉空间场景的深度学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00044

Wenkai Li, Theo Gueuret, Beiyu Lin

{"title":"Poster: SlideCNN: Deep Learning for Auditory Spatial Scenes with Limited Annotated Data","authors":"Wenkai Li, Theo Gueuret, Beiyu Lin","doi":"10.1109/SEC54971.2022.00044","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00044","url":null,"abstract":"Sound is an important modality to perceive and understand the spatial environment. With the development of digital technology, massive amounts of smart devices in use around the world can collect sound data. Auditory spatial scenes, a spatial environment to understand and distinguish sound, are important to be detected by analyzing sounds collected via those devices. Given limited annotated auditory spatial samples, the current best-performing model can predict an auditory scene with an accuracy of 73%. We propose a novel yet simple Sliding Window based Convolutional Neural Network, SlideCNN, without manually designing features. SlideCNN leverages windowing operation to increase samples for limited annotation problems and improves the prediction accuracy by over 12% compared to the current best-performing models. It can detect real-life indoor and outdoor scenes with a 85% accuracy. The results will enhance practical applications of ML to analyze auditory scenes with limited annotated samples. It will further improve the recognition of environments that may potentially influence the safety of people, especially people with hearing aids and cochlear implant processors.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"38 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":"131387202","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

Secure KNN Set Similarity Search in Outsourced Cloud Environments 外包云环境中的安全KNN集相似度搜索

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00072

Lu Li, Xufeng Jiang, Ge Gao

引用次数: 0

Discovering Onion Services Through Circuit Fingerprinting Attacks 通过电路指纹攻击发现洋葱服务

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00076

Bin Huang, Yanhui Du

{"title":"Discovering Onion Services Through Circuit Fingerprinting Attacks","authors":"Bin Huang, Yanhui Du","doi":"10.1109/SEC54971.2022.00076","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00076","url":null,"abstract":"Tor onion services provide anonymous service to clients using the Tor browser without disclosing the real address of the server. But an adversary could use a circuit fingerprinting attack to classify circuit types and discover the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks. But we found that circuits still expose much information to the adversary. In this paper, we present a novel circuit fingerprinting attack, which divides the circuit into the circuit generated by the client and the circuit generated by the onion service. To get a more effective attack, we tried three state-of-the-art classification models called SVM, Random Forest and XG-Boost, respectively. As the best performance, we attain 99.99 % precision and 99.99% recall when using Random Forest and X G Boost classification models, respectively. And we also tried to classify circuit types using our features and the classification model mentioned above, which was first proposed by Kwon. The best performance was achieved with 99.99% precision and 99.99% recall when using the random forest classifier in circuit type classification. The experimental results show that we achieved highly accurate circuit fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"41 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":"115960686","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

Research on Medical Data Storage and Sharing Model Based on Blockchain 基于区块链的医疗数据存储与共享模型研究

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00073

Jian Zhao, Wenqian Qiang, Zisong Zhao, Tianbo An, Zhejun Kuang, Dawei Xu, L. Shi

{"title":"Research on Medical Data Storage and Sharing Model Based on Blockchain","authors":"Jian Zhao, Wenqian Qiang, Zisong Zhao, Tianbo An, Zhejun Kuang, Dawei Xu, L. Shi","doi":"10.1109/SEC54971.2022.00073","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00073","url":null,"abstract":"With the process of medical informatization, medical diagnosis results are recorded and shared in the form of electronic data in the computer. However, the security of medical data storage can not be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that can not be underestimated. To solve the above problems, a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is constructed. Based on the idea of blockchain decentralization, the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions; A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data; Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy; A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is proposed. After security verification, compared with the traditional medical big data storage and sharing mode, the model has better security and sharing.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"6 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":"117165257","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

Towards Out-of-core Neural Networks on Microcontrollers 微控制器上的外核神经网络

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00008

Hongyu Miao, F. Lin

引用次数: 1

MicroSplit: Efficient Splitting of Microservices on Edge Clouds MicroSplit:在边缘云上高效地拆分微服务

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00027

A. Rahmanian, A. Ali-Eldin, B. Skubic, E. Elmroth

{"title":"MicroSplit: Efficient Splitting of Microservices on Edge Clouds","authors":"A. Rahmanian, A. Ali-Eldin, B. Skubic, E. Elmroth","doi":"10.1109/SEC54971.2022.00027","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00027","url":null,"abstract":"Edge cloud systems reduce the latency between users and applications by offloading computations to a set of small-scale computing resources deployed at the edge of the network. However, since edge resources are constrained, they can become saturated and bottlenecked due to increased load, resulting in an exponential increase in response times or failures. In this paper, we argue that an application can be split between the edge and the cloud, allowing for better performance compared to full migration to the cloud, releasing precious resources at the edge. We model an application's internal call-Graph as a Directed-Acyclic-Graph. We use this model to develop MicroSplit, a tool for efficient splitting of microservices between constrained edge resources and large-scale distant backend clouds. MicroSplit analyzes the dependencies between the microservices of an application, and using the Louvain method for community detection-a popular algorithm from Network Science-decides how to split the microservices between the constrained edge and distant data centers. We test MicroSplit with four microservice based applications in various realistic cloud-edge settings. Our results show that Microsplit migrates up to 60 % of the microservices of an application with a slight increase in the mean-response time compared to running on the edge, and a latency reduction of up to 800 % compared to migrating the entire application to the cloud. Compared to other methods from the State-of-the-Art, MicroSplit reduces the total number of services on the edge by up to five times, with minimal reduction in response times.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"44 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":"132912065","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

Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication 端到端无线电通信的混合量子经典自编码器

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00071

Zsolt Tabi, Bence Bako, Dániel T. R. Nagy, Péter Vaderna, Zsófia Kallus, Péter Hága, Z. Zimborás

引用次数: 0

Milestones on the Quantum Utility Highway 量子公用事业高速公路的里程碑

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00058

Catherine C. McGeoch, Pau Farré

引用次数: 0

Position Paper: Renovating Edge Servers with ARM SoCs 意见书:用ARM soc革新边缘服务器

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00024

Mengwei Xu, Li Zhang, Shangguang Wang

{"title":"Position Paper: Renovating Edge Servers with ARM SoCs","authors":"Mengwei Xu, Li Zhang, Shangguang Wang","doi":"10.1109/SEC54971.2022.00024","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00024","url":null,"abstract":"Edge servers are key to the success of edge computing. Compared to cloud servers, edge servers suffer from a more constrained and costly electricity supply due to their dense, near-population deployment. Towards higher energy efficiency, we propose an extreme design of edge servers - SoC-Cluster that consists of massive, inter-connected ARM SoCs. Indeed, such SoC-Clusters have already been adopted to serve the cloud gaming application in the wild. In this paper, we present a concrete implementation of a COTS SoC-Cluster and its hardware specifications. We then discuss the potential killer applications that such SoC-Cluster can well serve and the major challenges to be solved. We also dive deep into two of such applications (live video transcoding and deep learning serving) and carry out a measurement study to demystify the application performance of SoC-Cluster. The results reveal that, compared to traditional servers, SoC-Cluster not only can reduce energy consumption but even deliver higher workload throughput in certain scenarios. Finally, we conclude the paper and discuss the primary research directions that can be explored by our community from applications, software, and hardware aspects.","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":"134426785","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

ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network ClairvoyantEdge:网络边缘点播视频的预见性预取

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00010

Manasvini Sethuraman, Anirudh Sarma, Adwait Bauskar, Ashutosh Dhekne, U. Ramachandran

{"title":"ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network","authors":"Manasvini Sethuraman, Anirudh Sarma, Adwait Bauskar, Ashutosh Dhekne, U. Ramachandran","doi":"10.1109/SEC54971.2022.00010","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00010","url":null,"abstract":"On-demand video contributes a large fraction of the data traffic on mobile networks. This share is expected to increase even more drastically in the coming years. While the cellular infrastructure is continuously evolving to keep pace with this increasing demand, it is necessary to ensure that sufficient bandwidth is reserved for other latency-sensitive realtime applications like video conferencing and multiplayer video games. A tangible approach involves reducing on-demand video load on cellular networks, especially from users on the move. We see an opportunity for cellular load reduction using edge nodes based on two observations: (1) video streaming is mostly a download-only operation with sequential data access; and (2) short-range mmWave links can deliver an extremely high throughput for nearby recipients of data. The knowledge of the user's planned travel route creates opportunities for prescient prefetching and delivering the content as the vehicle passes through just in time, using mmWave devices on en route edge nodes. ClairvoyantEdge is a novel networked system infrastructure that leverages inter-edge node communication and the knowledge of users' trajectories to plan and deliver buffered video segments to the vehicles passing by. To evaluate ClairvoyantEdge, we built a comprehensive end-to-end emulation-based workflow that incorporates in situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46km2 geographical area employing 20 edge nodes distributed in that area providing short-range mmWave access to passing vehicles, we achieve an average reduction of up to 21% in cellular bandwidth usage for video downloads, using a real-world workload comprising 758 vehicles. Our results validate the promise of ClairvoyantEdge for incorporation in future edge infrastructure evolution.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"29 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":"121055199","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