1373388 - IoT and Edge Computing for Real-time Pavement Condition Monitoring.

Real-time pavement condition monitoring provides highway agencies with timely and accurate information that could form the basis of pavement maintenance and management policies. Existing technologies for pavement condition monitoring rely heavily on manual data processing, are expensive and therefore, difficult to scale for frequent, network level pavement condition monitoring. Additionally, these systems require sending a large packets of data to the cloud which requires large storage space, are computationally expensive to process, and results in high latency. The current study proposes to leverage the proliferation of low-cost Micro Electro-Mechanical System (MEMS) sensors, edge computing and internet connection capabilities of microcontrollers and deployability of compressible machine learning models (Tiny ML) to (a) design an Internet of Things (IoT)-enabled device that can be mounted on axles of vehicles to stream live pavement condition data (b) reduce latency through on-device processing and analytics of pavement condition sensor data before sending to the cloud servers (c) facilitate participatory crowd-sensing of pavement condition data at the societal level. Ultimately, the study provides a cost-effective low latency system that will reduce the burden of manual data processing and analytics via on-device edge computing and provide highway agencies and DOTs with real-time and accurate pavement condition information.