Proceedings of the 17th Conference on Embedded Networked Sensor Systems最新文献_第7页

Fine-grained travel time sensing in heterogeneous mobile networks: poster abstract 异构移动网络中的细粒度旅行时间感知:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361955

Zhihan Fang, Fan Zhang, Desheng Zhang

{"title":"Fine-grained travel time sensing in heterogeneous mobile networks: poster abstract","authors":"Zhihan Fang, Fan Zhang, Desheng Zhang","doi":"10.1145/3356250.3361955","DOIUrl":"https://doi.org/10.1145/3356250.3361955","url":null,"abstract":"Sensing and estimating real-time passenger travel time in urban mobile networks provides essential information for passengers to select different transportation modalities, improving their travel experience. Most existing work on travel time sensing has been focused on individual transportation modalities and its riding time based on the static time tables. However, passengers often consider different modes of transportation, e.g. taxis, subways, buses or personal vehicles, and a significant portion of the travel time is spent in the uncertain waiting or walking, which cannot be accurately obtained by static time tables. In this paper, we design a real-time data-driven framework FineTravel for fine-grained travel time sensing based on multi-source real-time data from multi-modal mobile networks including taxi, bus, subway, and private vehicle. The key challenge we address in FineTravel is to estimate implicit components (including walking, waiting and riding time) of the total travel time without direct measurement. In contrast to the existing work based on single-modal transportation networks mostly with offline data, the novelty of the FineTravel is based on its real-time multi-source data-driven (including both vehicle GPS and smartcard data) modeling for a comprehensive travel time sensing.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133892735","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

Ultrasonic waste monitoring in the future industrial kitchen: poster abstract 未来工业厨房的超声波废物监测:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361960

Fábio Vasconcelos, Vitor Aguiar, Lucas Pereira

引用次数: 2

Side channel attack on smartphone sensors to infer gender of the user: poster abstract 侧信道攻击智能手机传感器，以推断用户的性别:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361939

S. Singh, D. Shila, Gail E. Kaiser

引用次数: 6

WideSee

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3360031

Lili Chen, Jie Xiong, Xiaojiang Chen, S. Lee, Kai Chen, Dianhe Han, Dingyi Fang, Zhanyong Tang, Zheng Wang

{"title":"WideSee","authors":"Lili Chen, Jie Xiong, Xiaojiang Chen, S. Lee, Kai Chen, Dianhe Han, Dingyi Fang, Zhanyong Tang, Zheng Wang","doi":"10.1145/3356250.3360031","DOIUrl":"https://doi.org/10.1145/3356250.3360031","url":null,"abstract":"Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. WideSee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by the device mobility and LoRa's high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated WideSee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of WideSee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117064350","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: 53

Towards wide-area contactless human sensing: poster abstract 面向广域非接触式人体传感:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361931

Kai Chen, Lili Chen, Dianhe Han, Jie Xiong, S. Lee, Xiaojiang Chen, Zhanyong Tang, Dingyi Fang, Fuwei Wang, Z. Wang

引用次数: 4

Stardust: A deep learning serving system in IoT: demo abstract 星尘:物联网中的深度学习服务系统:演示摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361962

Shuochao Yao, Tianshi Wang, Jinyang Li, T. Abdelzaher

{"title":"Stardust: A deep learning serving system in IoT: demo abstract","authors":"Shuochao Yao, Tianshi Wang, Jinyang Li, T. Abdelzaher","doi":"10.1145/3356250.3361962","DOIUrl":"https://doi.org/10.1145/3356250.3361962","url":null,"abstract":"The deep neural network becomes an increasingly crucial component in recent intelligent applications. The excessive resource consumptions of state-of-the-art neural networks, however, remains a huge impediment towards their widespread deployment in the Internet of Things (IoT). In this paper, we propose an IoT-oriented deep learning serving system, Stardust, that accelerates the neural network inference to improve the quality of IoT services. Stardust integrates several joint contributions from both the system and AI perspectives, including system performance predictor, model compression, and compressive offloading. On one hand, the performance predictor profiles and predicts the runtime characteristics of neural network operations on a particular device with the targeted runtime environment, which enables a hardware and software oriented performance optimization during model compression and offloading. On the other hand, the model compression minimizes the computation time of neural networks on different devices, and the compressive offloading diminishes the network data transferring time during the mobile-edge offloading. Moreover, all these optimizations can be done with almost no compromise on inference accuracy. The integration of these modules, therefore, collaboratively reduce the end-to-end latency of serving deep learning services that reside across embedded/mobile devices and edge servers. We deploy illustrative applications on Stardust, performing human perception tasks with on-device camera microphone and motion sensors to demonstrate the capability of Stardust serving system.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125590583","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

Edge-assisted collaborative image recognition for augmented reality: demo abstract 边缘辅助的增强现实协同图像识别:演示摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361944

Jovan Stojkovic, Zida Liu, Guohao Lan, Carlee Joe-Wong, M. Gorlatova

引用次数: 5

Proceedings of the 17th Conference on Embedded Networked Sensor Systems 第17届嵌入式网络传感器系统会议论文集

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 1900-01-01 DOI: 10.1145/3356250

引用次数: 2