Exploring the Need for Sensor Learning and Collaboration in IoT-based Parking Systems
Journal
SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
Pages
423-424
Date Issued
2015
Author(s)
Yu Huang
Dian-Xuan Wu
Chi-Ling Yang
Seng-Yong Lau
Kai-Lung Hua
Wen-Huang Cheng
Yi-Ling Chen
Jane Yung-Jen Hsu
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
Publisher
Association for Computing Machinery
Type
poster