Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract

Daniel Yue Zhang; Dong Wang

doi:10.1145/3302505.3312589

Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract

Daniel Yue Zhang, Dong Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Social sensing has emerged as a new application paradigm where measurements about the physical world are collected from humans or devices on their behalf. One of the representative application of social sensing is disaster damage assessment (DDA) that automatically identifies damage severity of impacted areas from imagery reports reported by eyewitness in the aftermath of a disaster (e.g., earthquake, hurricane, landslides). In this demo, we present a Social Sensing based Edge Computing system (SSEC) that can coordinate the privately owned IoT devices in close proximity of the disaster scene to collect, process and report the real-time status of the disaster. We showcase a supply chain-based resource management framework for SSEC that tames the pronounced run-time and hardware heterogeneity of the IoT devices at the edge to provide reliable sensing and computing power. The system is demonstrated on a real-world hardware platform consists of a diverse set of heterogeneous embedded systems.

Original language	English (US)
Title of host publication	IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation
Editors	Gowri Sankar Ramachandran, Jorge Ortiz
Publisher	Association for Computing Machinery, Inc
Pages	269-270
Number of pages	2
ISBN (Electronic)	9781450362832
DOIs	https://doi.org/10.1145/3302505.3312589
State	Published - Apr 15 2019
Externally published	Yes
Event	4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019 - Montreal, Canada Duration: Apr 15 2019 → Apr 18 2019

Publication series

Name	IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation

Conference

Conference	4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019
Country/Territory	Canada
City	Montreal
Period	4/15/19 → 4/18/19

Keywords

disaster damage assessment
disaster response
edge computing
resource management
social sensing

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Online availability

10.1145/3302505.3312589

Library availability

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Zhang, D. Y., & Wang, D. (2019). Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract. In G. S. Ramachandran, & J. Ortiz (Eds.), IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation (pp. 269-270). (IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation). Association for Computing Machinery, Inc. https://doi.org/10.1145/3302505.3312589

Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract. / Zhang, Daniel Yue; Wang, Dong.
IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation. ed. / Gowri Sankar Ramachandran; Jorge Ortiz. Association for Computing Machinery, Inc, 2019. p. 269-270 (IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, DY & Wang, D 2019, Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract. in GS Ramachandran & J Ortiz (eds), IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation. IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation, Association for Computing Machinery, Inc, pp. 269-270, 4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019, Montreal, Canada, 4/15/19. https://doi.org/10.1145/3302505.3312589

Zhang DY, Wang D. Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract. In Ramachandran GS, Ortiz J, editors, IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation. Association for Computing Machinery, Inc. 2019. p. 269-270. (IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation). doi: 10.1145/3302505.3312589

Zhang, Daniel Yue ; Wang, Dong. / Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response : Demo Abstract. IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation. editor / Gowri Sankar Ramachandran ; Jorge Ortiz. Association for Computing Machinery, Inc, 2019. pp. 269-270 (IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation).

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abstract = "Social sensing has emerged as a new application paradigm where measurements about the physical world are collected from humans or devices on their behalf. One of the representative application of social sensing is disaster damage assessment (DDA) that automatically identifies damage severity of impacted areas from imagery reports reported by eyewitness in the aftermath of a disaster (e.g., earthquake, hurricane, landslides). In this demo, we present a Social Sensing based Edge Computing system (SSEC) that can coordinate the privately owned IoT devices in close proximity of the disaster scene to collect, process and report the real-time status of the disaster. We showcase a supply chain-based resource management framework for SSEC that tames the pronounced run-time and hardware heterogeneity of the IoT devices at the edge to provide reliable sensing and computing power. The system is demonstrated on a real-world hardware platform consists of a diverse set of heterogeneous embedded systems.",

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note = "Funding Information: This research is supported in part by the National Science Foundation under Grant No. CNS-1831669, CBET-1637251, CNS-1566465 and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409, Google 2017 Faculty Research Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 2019 Authors.; 4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019 ; Conference date: 15-04-2019 Through 18-04-2019",

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Heterogeneous Social Sensing Edge Computing System for Deep Learning Based Disaster Response: Demo Abstract

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