On the Latency Performance of Mobile Mapping Services: Towards Vulnerable Road Users Safety

In this work, we investigate the performance and suitability of various mobile frameworks for critical smart city applications. With the increasing reliance on mobile apps for urban services, understanding the capabilities and assessing the limitations of different development platforms is crucial. We evaluate Android OS, Apple iOS, and cross-platform solutions like Flutter, focusing on their ability to deliver real-time information efficiently in mobile applications tailored for smart city environments. In our work, we look at the content delivery latency, scalability, cross-platform capabilities, and developer productivity. We also assess the frameworks' integration with existing smart city infrastructure and their handling of distributed location-dependent data. The study compares native implementations using platform-specific SDKs against cross-platform solutions, with particular attention to map-based applications utilizing both native maps and third-party services like Mapbox. We have tested the platforms within the Modena Automotive Smart Area (MASA), a real-world testbed for innovative mobility solutions in Modena, Italy. This environment provided a realistic setting to evaluate the frameworks' performance in critical smart city scenarios. The outcomes of our work offer insights into the relative strengths and limitations of each mobile framework, considering both technical performance and development efficiency. In this work, we aim to guide developers and city planners in selecting the most appropriate mobile framework for smart city applications, balancing performance requirements with development resources and cross-platform needs.

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Publication Details

Published
2025-01-10
DOI
https://doi.org/10.1109/ccnc54725.2025.10976011
Citations
1
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
1.28

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article

On the Latency Performance of Mobile Mapping Services: Towards Vulnerable Road Users Safety

Carlo Augusto Grazia, Luca Bedogni, Raoul Scalise
1 citations
Context-Aware Activity Recognition Systems
1.28
article

On the Latency Performance of Mobile Mapping Services: Towards Vulnerable Road Users Safety

Carlo Augusto Grazia, Luca Bedogni, Raoul Scalise
article en
1 citations

Abstract

In this work, we investigate the performance and suitability of various mobile frameworks for critical smart city applications. With the increasing reliance on mobile apps for urban services, understanding the capabilities and assessing the limitations of different development platforms is crucial. We evaluate Android OS, Apple iOS, and cross-platform solutions like Flutter, focusing on their ability to deliver real-time information efficiently in mobile applications tailored for smart city environments. In our work, we look at the content delivery latency, scalability, cross-platform capabilities, and developer productivity. We also assess the frameworks' integration with existing smart city infrastructure and their handling of distributed location-dependent data. The study compares native implementations using platform-specific SDKs against cross-platform solutions, with particular attention to map-based applications utilizing both native maps and third-party services like Mapbox. We have tested the platforms within the Modena Automotive Smart Area (MASA), a real-world testbed for innovative mobility solutions in Modena, Italy. This environment provided a realistic setting to evaluate the frameworks' performance in critical smart city scenarios. The outcomes of our work offer insights into the relative strengths and limitations of each mobile framework, considering both technical performance and development efficiency. In this work, we aim to guide developers and city planners in selecting the most appropriate mobile framework for smart city applications, balancing performance requirements with development resources and cross-platform needs.

University of Modena and Reggio Emilia (IT)
European Commission
Openalex Percentile: Top 21%
Context-Aware Activity Recognition Systems
1.28
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