Exploiting geospatial data of connectivity and urban infrastructure for efficient positioning of emergency detection units in smart cities

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Peixoto, JPJ (Author) Other; Bittencourt, JCN (Author) Other; Jesus, TC (Author) FEUP; Costa, DG (Author) FEUP; Paulo Portugal (Author) FEUP; Francisco Vasques (Author) FEUP

Journal

Title: Computers, Environment and Urban Systems

Vol. 107

ISSN: 0198-9715

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00Z-E9M

DOI: 10.1016/j.compenvurbsys.2023.102054

Abstract (EN): The detection of critical situations through the adoption of multi-sensor Emergency Detection Units (EDUs) can significantly reduce the time between the initial stages of urban emergencies and the actual responses to relieve its negative effects, usually through the rescuing of endangered people, the attending to eventual victims, and the mitigating of its causes. However, although the benefits of such units are well known, their proper positioning in a city is challenging when considering a limited set of available units. In this sense, data-driven approaches can be leveraged to provide a better perception of the urban environments under consideration, allowing emergency management systems to be tailored to the specificities of a target city, thus improving the positioning of EDUs. This article proposes the processing of geospatial data of emergency-related urban infrastructure to support the computing of risk zones in a city, which is retrieved from the OpenStreetMap database together with the map of streets within a defined area. Since risk zones indirectly indicate the proportional number of detection units to be deployed, for each configuration setting of the EDUs, we propose an algorithm that computes the positions for such units only on streets, in a balanced way. Furthermore, considering that EDUs are expected to report detected emergencies through a wireless connection, we have also modelled the coverage area of existing networks in a city, which is also processed according to a suitable dataset. The proposed algorithm performs a fine-grained positioning of EDUs based on the number of active networks, flexibly favouring the EDUs' connectivity re-quirements such as reliability, throughput, latency, and transmission costs according to the actual demands of any urban emergency management system. Experimental results with real data demonstrated the applicability of the proposed mathematical model and the associated algorithm, reinforcing its practical application for the planning and construction of smart cities.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19