research-article

Edge computing enabled smart firefighting: opportunities and challenges

Authors:

Qingyang Zhang,

Weisong ShiAuthors Info & Claims

HotWeb '17: Proceedings of the fifth ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies

Article No.: 11, Pages 1 - 6

https://doi.org/10.1145/3132465.3132475

Published: 14 October 2017 Publication History

Abstract

By collectively leveraging advanced communications systems, sensing, drones, wearable technologies and large-scale data analysis, smart firefighting is envisioned as the next generation firefighting with the capacities of gathering massive real-time scene data, transferring them into useful information and insights for fire responders, and even providing them with more safe and accurate decisions. For smart firefighting, timeliness and accuracy are two foremost system requirements, yet they are unsatisfied in many applications. One reason for such dilemma is due to the underlying used computing architecture (i.e. cloud computing) that can produce extra latency in large-scale data transmission. To address this problem, we explore the firefighting field utilizing edge computing and discuss the overall system architecture, opportunities, challenges, as well as some early technical suggestions on building edge-enabled smart firefighting. To validate the feasibility of edge computing, we simulate the firefighting context and respectively deploy a video-based flame detection algorithm on a local Intel's edge computing platform and a remote Amazon EC2. The preliminary results show that edge computing can significantly increase system's reactive speed, with on average 50% reduction in system latency.

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Aksüt GEren T(2024)Evaluation of wearable device technology in terms of health and safety in firefightersTechnology and Health Care10.1177/09287329241291385Online publication date: 7-Nov-2024
https://doi.org/10.1177/09287329241291385
Sharma MTomar AHazra A(2024)Edge Computing for Industry 5.0: Fundamental, Applications, and Research ChallengesIEEE Internet of Things Journal10.1109/JIOT.2024.335929711:11(19070-19093)Online publication date: 1-Jun-2024
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Index Terms

Edge computing enabled smart firefighting: opportunities and challenges
1. Computer systems organization
  1. Architectures
    1. Distributed architectures

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cover image ACM Conferences

HotWeb '17: Proceedings of the fifth ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies

October 2017

97 pages

ISBN:9781450355278

DOI:10.1145/3132465

Program Chairs:
Qun Li
College of William and Mary
,
Songqing Chen
George Mason University

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 October 2017

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SEC '17: IEEE/ACM Symposium on Edge Computing

October 14, 2017

California, San Jose

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Cited By

Cheng HZhu JWang SYan KWang H(2024)A Study on Predicting the Deviation of Jet Trajectory Falling Point under the Influence of Random WindSensors10.3390/s2411346324:11(3463)Online publication date: 27-May-2024
https://doi.org/10.3390/s24113463
Aksüt GEren T(2024)Evaluation of wearable device technology in terms of health and safety in firefightersTechnology and Health Care10.1177/09287329241291385Online publication date: 7-Nov-2024
https://doi.org/10.1177/09287329241291385
Sharma MTomar AHazra A(2024)Edge Computing for Industry 5.0: Fundamental, Applications, and Research ChallengesIEEE Internet of Things Journal10.1109/JIOT.2024.335929711:11(19070-19093)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JIOT.2024.3359297
Pitstick KNovakouski MLewis GOzkaya I(2024)Defining a Reference Architecture for Edge Systems in Highly-Uncertain Environments2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C)10.1109/ICSA-C63560.2024.00064(356-361)Online publication date: 4-Jun-2024
https://doi.org/10.1109/ICSA-C63560.2024.00064
Renu Sithole T(2024)Role of Soft Computing in Industry 4.0 and 5.0Soft Computing in Industry 5.0 for Sustainability10.1007/978-3-031-69336-6_2(45-59)Online publication date: 16-Nov-2024
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S-Julián RLacalle IVaño RBoronat FPalau C(2023)Self-* Capabilities of Cloud-Edge Nodes: A Research ReviewSensors10.3390/s2306293123:6(2931)Online publication date: 8-Mar-2023
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Berta RBellotti FDe Gloria ALazzaroni L(2022)Assessing Versatility of a Generic End-to-End Platform for IoT Ecosystem ApplicationsSensors10.3390/s2203071322:3(713)Online publication date: 18-Jan-2022
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Gudepu VPappu BJavvadi TBassoli RFitzek FValcarenghi LDevi DKondepu K(2022)Edge Computing in Micro Data Centers for Firefighting in Residential Areas of Future Smart Cities2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME55909.2022.9988101(1-6)Online publication date: 16-Nov-2022
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Schwartz DSelman JWrege PPaepcke A(2021)Deployment of Embedded Edge-AI for Wildlife Monitoring in Remote Regions2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00170(1035-1042)Online publication date: Dec-2021
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