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Heterogeneous social sensing edge computing system for deep learning based disaster response: demo abstract

Authors:

Daniel (Yue) Zhang,

Dong WangAuthors Info & Claims

IoTDI '19: Proceedings of the International Conference on Internet of Things Design and Implementation

Pages 269 - 270

https://doi.org/10.1145/3302505.3312589

Published: 15 April 2019 Publication History

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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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Cited By

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Vignesh UGokul Ram KAl-Obaidi A(2024)Optimizing Resource Management With Edge and Network Processing for Disaster Response Using Insect Robot SwarmsExploring the Micro World of Robotics Through Insect Robots10.4018/979-8-3693-6150-4.ch003(39-60)Online publication date: 20-Sep-2024
https://doi.org/10.4018/979-8-3693-6150-4.ch003
Wang DZhang DWang DZhang D(2023)Real-Time AI in Social EdgeSocial Edge Computing10.1007/978-3-031-26936-3_5(71-96)Online publication date: 20-Feb-2023
https://doi.org/10.1007/978-3-031-26936-3_5
Zhang DZhang YLi QPlummer TWang D(2019)CrowdLearn: A Crowd-AI Hybrid System for Deep Learning-based Damage Assessment Applications2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2019.00123(1221-1232)Online publication date: Jul-2019
https://doi.org/10.1109/ICDCS.2019.00123

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Heterogeneous social sensing edge computing system for deep learning based disaster response: demo abstract

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Information & Contributors

Information

Published In

IoTDI '19: Proceedings of the International Conference on Internet of Things Design and Implementation

April 2019

299 pages

ISBN:9781450362832

DOI:10.1145/3302505

General Chairs:
Olaf Landsiedel
Univ. of Kiel,Germany,Chalmers Univ. of Technology, Sweden
,
Klara Nahrstedt
University of Illinois Urbana-Champaign

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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IEEE CS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 April 2019

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Demonstration

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IoTDI '19

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SIGBED

IoTDI '19: International Conference on Internet-of-Things Design and Implementation

April 15 - 18, 2019

Quebec, Montreal, Canada

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Cited By

View all

Vignesh UGokul Ram KAl-Obaidi A(2024)Optimizing Resource Management With Edge and Network Processing for Disaster Response Using Insect Robot SwarmsExploring the Micro World of Robotics Through Insect Robots10.4018/979-8-3693-6150-4.ch003(39-60)Online publication date: 20-Sep-2024
https://doi.org/10.4018/979-8-3693-6150-4.ch003
Wang DZhang DWang DZhang D(2023)Real-Time AI in Social EdgeSocial Edge Computing10.1007/978-3-031-26936-3_5(71-96)Online publication date: 20-Feb-2023
https://doi.org/10.1007/978-3-031-26936-3_5
Zhang DZhang YLi QPlummer TWang D(2019)CrowdLearn: A Crowd-AI Hybrid System for Deep Learning-based Damage Assessment Applications2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2019.00123(1221-1232)Online publication date: Jul-2019
https://doi.org/10.1109/ICDCS.2019.00123

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