Exploring Approaches to Improve the Performance of Autonomous Crane Safety Monitoring with Imperfect Data in Location-aware Wireless Sensor Networks

In recent years, information and sensing technologies have been applied to the construction industry to collect and provide rich information to facilitate decision making processes. One of the applications is using location data to support autonomous crane safety monitoring (e.g., collision avoidance and dangerous areas control). Several location-aware wireless technologies such as GPS (Global Positioning System), RFID (Radio-frequency identification), and Ultra-Wide Band sensors, have been proposed to provide location information for autonomous safety monitoring. However, previous studies indicated that imperfections (errors, uncertainty, and inconsistency) exist in the data collected from those sensors and the data imperfections have great impacts on autonomous safety monitoring system performance. This paper explores five light-weight approaches to deal with the data imperfections, aiming to improve the system performance. The authors built a scaled autonomous crane safety monitoring testbed with a mounted localization system to collect location data and developed five representative test cases based on a live construction jobsite. Seven hundred and sixty location readings were collected at thirty eight test points from the sensors. Those location data was feed into the reasoning mechanisms with five approaches to generate the safety decisions at those thirty eight test points and evaluate system performance in terms of precision, recall and accuracy. The results indicate that system performance can be somewhat improved if at least 10 simultaneous location readings are collected at the same point. However, by including additional data such as velocity and acceleration that may be read from devices mounted on workers, localization error may be significantly reduced.