Poster: Exploring the need for sensor learning and collaboration in IoT-based parking systems

Yu Huang, Dian Xuan Wu, Chuang Wen You, Chi Ling Yang, Seng Yong Lau, Kai Lung Hua, Wen-Huang Cheng, Yi Ling Chen, Jane Yung Jen Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The need to find parking contributes to road congestion and leads to unnecessary fuel consumption. Of all emerging parking systems, Internet-of-Things (IoT)-based systems have demonstrated the feasibility of real-time delivery of parking availability using magnetic sensors. However, existing magnetic-based methods are prone to false positives caused by electromagnetic fields emitted from surrounding electric facilities. In this study, we conducted a 3-month data collection in a parking area. We identified the need to introduce learning and collaboration into the design of our detection algorithm which recognizes learned patterns associated with car arrivals or departures, and to filter out unreliable events based on spatial and temporal features.

Original languageEnglish
Title of host publicationSenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages423-424
Number of pages2
ISBN (Electronic)9781450336314
DOIs
StatePublished - 1 Nov 2015
Event13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015 - Seoul, Korea, Republic of
Duration: 1 Nov 20154 Nov 2015

Publication series

NameSenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

Conference

Conference13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015
CountryKorea, Republic of
CitySeoul
Period1/11/154/11/15

Keywords

  • Internet of Things (IoT)
  • Magnetic sensing
  • Smart parking

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