2019 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第6页

Demonstration of Federated Learning in a Resource-Constrained Networked Environment 资源受限网络环境下的联邦学习论证

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00095

D. Conway-Jones, Tiffany Tuor, Shiqiang Wang, K. Leung

{"title":"Demonstration of Federated Learning in a Resource-Constrained Networked Environment","authors":"D. Conway-Jones, Tiffany Tuor, Shiqiang Wang, K. Leung","doi":"10.1109/SMARTCOMP.2019.00095","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00095","url":null,"abstract":"Many modern applications in the area of smart computing are based on machine learning techniques. To train machine learning models, a large amount of data is usually required, which is often not readily available at a central location. Federated learning enables the training of machine learning models from distributed datasets at client devices without transmitting the data to a central place, which has benefits including preserving the privacy of user data and reducing communication bandwidth. In this demonstration, we show a federated learning system deployed in an emulated wide-area communications network with dynamic, heterogeneous, and intermittent resource availability, where the network is emulated using a CORE/EMANE emulator. In our system, the environment is decentralized and each client can ask for assistance by other clients. The availability of clients is intermittent so only those clients that are available can provide assistance. A graphical interface illustrates the network connections and the user can adjust these connections through the interface. A user interface displays the training progress and each client's contribution to training.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125103276","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: 24

BITS 2019 Committees BITS 2019委员会

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00010

引用次数: 0

Patient Self-Assessment of 3D Printed Upper-Extremity Prosthetics 3D打印上肢义肢患者自我评估

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00054

Joshua V. Gyllinsky, James Gannon, Corvah Akoiwala, Cristian R. Witcher, Laura Parra, J. Baez, Travis Frink, Brendan Driscoll, Jairo Orduz, Zaid C. Aguanche, Isaac Beleño, Gustavo Pérez, Carlos Roa, Omar Gutiérrez, C. EfrenH.Garcia, Silke Scholz, K. Mankodiya

引用次数: 1

Tracking Underground Moving Targets with Wireless Seismic Networks 利用无线地震台网跟踪地下运动目标

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00082

Sili Wang, Fangyu Li, Maria Valero, José Clemente, Wenzhan Song

引用次数: 3

SSC 2019 Workshop Committees SSC 2019研讨会委员会

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00017

引用次数: 0

Anomaly Detection: Under the [data] Hood in Smart Cars 异常检测:智能汽车的[数据]引擎盖下

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00041

Faisal Quader, V. Janeja

引用次数: 0

Virtual Study Partner: A Cognitive Training Tool in Education 虚拟学习伙伴:教育中的认知训练工具

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00052

R. D. Pietro, Domenico Giacomo Campanile, S. Distefano

{"title":"Virtual Study Partner: A Cognitive Training Tool in Education","authors":"R. D. Pietro, Domenico Giacomo Campanile, S. Distefano","doi":"10.1109/SMARTCOMP.2019.00052","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00052","url":null,"abstract":"The ways of giving, receiving, processing and storing knowledge are changing rapidly. In a few years, in fact, the Web has radically changed our daily lives, making it indispensable for the most disparate reasons, from work to communication and learning. The availability of unlimited knowledge more and more often doesn't correspond to an improvement in school productivity of the new generations. All the problems stem from the fact that the social and educational systems have not evolved with the same speed as the technology. Moreover, the economic crisis has negatively affected this problem. Today it is increasingly difficult for adults to be present at home and help their children do their homework activities in the early school years. In this paper, we present and discuss our Android mobile application we called \"Virtual Study Partner\". This mobile application has to be considered as the first output of a wider research activity that aims to provide study support to young people in school-age by integrating both machine learning concepts and Cloud technologies. Virtual Study Partner consists of a cognitive training tool which enables and integrates the experimental Learning Framework that we are developing at the University of Messina.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121214436","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

Cost-Effective Sensor Data Collection from Internet-of-Things Zones Using Existing Transportation Fleets 利用现有运输车队从物联网区域收集具有成本效益的传感器数据

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00071

Fangqi Liu, Qiuxi Zhu, M. Y. S. Uddin, Cheng-Hsin Hsu, N. Venkatasubramanian

{"title":"Cost-Effective Sensor Data Collection from Internet-of-Things Zones Using Existing Transportation Fleets","authors":"Fangqi Liu, Qiuxi Zhu, M. Y. S. Uddin, Cheng-Hsin Hsu, N. Venkatasubramanian","doi":"10.1109/SMARTCOMP.2019.00071","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00071","url":null,"abstract":"Modern IoT devices are equipped with media-rich sensors that generate a heavy burden to local access networks. To improve the efficiency of data collection, we introduce the concept of \"IoT zones\" as geographically-correlated clusters of local IoT devices with well connected wireless networks that may have limited access to the Internet. We develop techniques to create a cost-effective data collection network using existing transportation fleets with predefined schedules to collect sensor data from IoT zones and upload them at locations with better network connectivity. Specifically, we provide solutions to the upload point placement and upload path planning problems given tradeoffs between collection quality, timing needs (QoS), and installation cost. We evaluate our approaches using a real-world bus network in Orange County, CA and study the applicability and efficiency of the proposed method as compared to several other approaches. The trace-driven simulations reveal that our best-performing algorithm: upload point selection (UPS) algorithm significantly outperforms others, e.g., in one of the scenarios with 160 total cost, it achieves sub-21 sec data transfer time (15+ times improvement), sub 3.2% late delivery ratio (about 12 times improvement), and above 96% data delivery ratio (about 50% improvement). In addition, it achieves the above performance without excessive installation cost: even when a cost limit of 640 is given, UPS algorithm opts for a solution with about 160 total cost (versus 640 from others).","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132560851","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

SMARTCOMP 2019 Keynote Abstracts SMARTCOMP 2019主题演讲摘要

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00008

引用次数: 0

Towards Reliability in Online High-Churn Edge Computing: A Deviceless Pipelining Approach 面向在线高流失率边缘计算的可靠性:一种无设备流水线方法

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00066

Nathan Vance, Md. Tahmid Rashid, D. Zhang, Dong Wang

{"title":"Towards Reliability in Online High-Churn Edge Computing: A Deviceless Pipelining Approach","authors":"Nathan Vance, Md. Tahmid Rashid, D. Zhang, Dong Wang","doi":"10.1109/SMARTCOMP.2019.00066","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00066","url":null,"abstract":"Social sensing based Edge Computing (SSEC) is an emerging application paradigm for rich context awareness in sensing applications in which people collaborate in both data acquisition and processing at the edge of the network. While keeping people in the loop can be an immense benefit, the unreliability and churn introduced can pose a dangerous stability threat for long-running SSEC applications. In the past this problem was addressed by offloading these responsibilities to more reliable server hardware in the cloud or a fog layer, however, this does not take full advantage of the power at the edge. In this paper we address the issue of reliable edge computing on dynamic high-churn edge systems. We develop a deviceless pipeline based approach (DPA) to establish workflows in which stages of the analysis pipeline are completed on edge devices, and any devices that leave the system can be replaced without data loss. We evaluate the performance of our system on a real-world edge computing system performing an object detection application and demonstrate that it can provide significant performance gains over traditional computation offloading schemes in terms of throughput and error recovery.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128650302","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: 20