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Lookup NU author(s): Kristina Wolf, Professor Richard DawsonORCiD, Professor Jon MillsORCiD, Professor Phil BlytheORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Cost-effective on-demand computing resources can help to process the increasing number of large, diverse datasets generated from smart internet-enabled technology, such as sensors, CCTV cameras, and mobile devices, with high temporal resolution. Category 1 emergency services (Ambulance, Fire and Rescue, and Police) can benefit from access to (near) real-time traffic- and weather data to coordinate multiple services, such as reassessing a route on the transport network affected by flooding or road incidents. However, there is a tendency not to utilise available smart city data sources, due to the heterogeneous data landscape, lack of real-time information, and communication inefficiencies. Using a systems engineering approach, we identify the current challenges faced by stakeholders involved in incident response and formulate future requirements for an improved system. Based on these initial findings, we develop a use case using Microsoft Azure cloud computing technology for analytical functionalities that can better support stakeholders in their response to an incident. Our prototype allows stakeholders to view available resources, send automatic updates and integrate location-based real-time weather and traffic data. We anticipate our study will provide a foundation for the future design of a data ontology for multi-agency incident response in smart cities of the future.
Author(s): Wolf K, Dawson RJ, Mills JP, Blythe P, Morley J
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2022
Volume: 12
Online publication date: 28/09/2022
Acceptance date: 09/09/2022
Date deposited: 10/10/2022
ISSN (electronic): 2045-2322
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41598-022-20178-8
DOI: 10.1038/s41598-022-20178-8
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