A resilient and distributed near real-time traffic forecasting application for Fog computing environments
| Autor: | David Carrera, Alberto Gutierrez-Torre, Josep Lluis Berral, Juan Luis Perez |
|---|---|
| Přispěvatelé: | Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Big Data
Internet of things IoT Analytics Computer Networks and Communications Computer science Real-time computing Big data Cloud computing 02 engineering and technology Informàtica [Àrees temàtiques de la UPC] 11. Sustainability 0202 electrical engineering electronic engineering information engineering Leverage (statistics) Resource management business.industry Deep learning Dades massives 020206 networking & telecommunications Floating car data Estimation of distribution algorithm Hardware and Architecture Fog computing 020201 artificial intelligence & image processing Artificial intelligence business Software |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Future Generation Computer Systems Recercat. Dipósit de la Recerca de Catalunya instname |
| DOI: | 10.1016/j.future.2018.05.013 |
| Popis: | In this paper we propose an architecture for a city-wide traffic modeling and prediction service based on the Fog Computing paradigm. The work assumes an scenario in which a number of distributed antennas receive data generated by vehicles across the city. In the Fog nodes data is collected, processed in local and intermediate nodes, and finally forwarded to a central Cloud location for further analysis. We propose a combination of a data distribution algorithm, resilient to back-haul connectivity issues, and a traffic modeling approach based on deep learning techniques to provide distributed traffic forecasting capabilities. In our experiments, we leverage real traffic logs from one week of Floating Car Data (FCD) generated in the city of Barcelona by a road-assistance service fleet comprising thousands of vehicles. FCD was processed across several simulated conditions, ranging from scenarios in which no connectivity failures occurred in the Fog nodes, to situations with long and frequent connectivity outage periods. For each scenario, the resilience and accuracy of both the data distribution algorithm, and the learning methods were analyzed. Results show that the data distribution process running in the Fog nodes is resilient to back-haul connectivity issues and is able to deliver data to the Cloud location even in presence of severe connectivity problems. Additionally, the proposed traffic modeling and forecasting method exhibits better behavior when run distributed in the Fog instead of centralized in the Cloud, especially when connectivity issues occur that force data to be delivered out of order to the Cloud. This project is partially supported by the European Research Council (ERC), Spain under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). The authors gratefully acknowledge the Reial Automvil Club de Catalunya (RACC) for the dataset of Floating Car Data provided. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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