Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

Abstract

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge-or cloud-only architectures.INDEX TERMS Edge-cloud architecture, IoT infrastructure, Edge computing, Urban computing, Smart cities, Urban mobility

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

“…In [35], authors presented an RSU (Roadside Unit) placement framework for V2I scenarios. The authors focused on determining the optimal locations for RSUs to effectively support V2I communication and services.…”

“…Belcastro et al [35] proposed an edge-cloud continuum architecture to address the challenges of urban mobility prediction and planning. They defined three application scenarios involving geotagged data from IoT objects and applied machine learning algorithms for tasks such as next location prediction, location-based advertising, and points of interest recommendation.…”

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

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