eprintid: 4711 rev_number: 7 eprint_status: archive userid: 2 dir: disk0/00/00/47/11 datestamp: 2022-11-22 23:30:04 lastmod: 2023-07-11 23:30:56 status_changed: 2022-11-22 23:30:04 type: article metadata_visibility: show creators_name: Anand, Divya creators_name: Singh, Aman creators_name: Alsubhi, Khalid creators_name: Goyal, Nitin creators_name: Abdrabou, Atef creators_name: Vidyarthi, Ankit creators_name: Rodrigues, Joel J. P. C. creators_id: divya.anand@uneatlantico.es creators_id: aman.singh@uneatlantico.es creators_id: creators_id: creators_id: creators_id: creators_id: title: A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: none keywords: Kernel adaptive filtering, Internet of Things, intelligent parking, parking prediction problem abstract: Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction. date: 2022-09 publication: IEEE Transactions on Intelligent Transportation Systems pagerange: 1-9 id_number: doi:10.1109/TITS.2022.3204352 refereed: TRUE issn: 1524-9050 official_url: http://doi.org/10.1109/TITS.2022.3204352 access: close language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Cerrado Inglés Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction. metadata Anand, Divya; Singh, Aman; Alsubhi, Khalid; Goyal, Nitin; Abdrabou, Atef; Vidyarthi, Ankit y Rodrigues, Joel J. P. C. mail divya.anand@uneatlantico.es, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction. IEEE Transactions on Intelligent Transportation Systems. pp. 1-9. ISSN 1524-9050