The Design of a Mobile Sensing Framework for Road Surfaces Based on Multi-Modal Sensors

Abstract

Road surface information, encompassing aspects like road surface damages and facility distributions, is vital for maintaining and updating roads in smart cities. The proposed mobile sensing framework uses multi-modal sensors, including a GPS, gyroscope, accelerometer, camera, and Wi-Fi, integrated into a Jetson Nano to collect comprehensive road surface information. The collected data are processed, stored, and analyzed on the server side, with results accessible via RESTful APIs. This system enables the detection of road conditions, which are visualized through the web mapping technique. Based on this concept, the Mobile Sensor Framework for Road Surface analysis (MSF4RS) is designed, and its use significantly enhances road surface data acquisition and analysis. Key contributions include (1) the integration of multi-modal IoT sensors to capture comprehensive road surface data; (2) the development of a software environment that facilitates robust data processing; and (3) the execution of experiments using the MSF4RS, which synergistically combines hardware and software components. The framework leverages advanced sensor technologies and server-based computational methods and offers a user-friendly web interface for the dynamic visualization and interactive exploration of road surface conditions. Experiments confirm the framework’s effectiveness in capturing and visualizing road surface data, demonstrating significant potential for smart city applications.

Keywords

• mobile sensing framework
• road surface
• multi-modal sensors
• RESTful APIs