Poster: Exploring the Need for Sensor Learning and Collaboration in IoT-based Parking Systems.
Journal
Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015, Seoul, South Korea, November 1-4, 2015
Pages
423-424
Date Issued
2015
Author(s)
Huang, Yu
Wu, Dian-Xuan
You, Chuang-Wen
Yang, Chi-Ling
Lau, Seng-Yong
Hua, Kai-Lung
Chen, Yi-Ling
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.
SDGs
Type
conference paper