Bret Hull

The pothole patrol: using a mobile sensor network for road surface monitoring

This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.

Understanding the real-world performance of carrier sense

Carrier sense is a fundamental part of most wireless networking stacks in wireless local area- and sensor networks. As increasing numbers of users and more demanding applications push wireless networks to their capacity limits, the efficacy of the carrier sense mechanism becomes a key factor in determining wireless network capacity.We describe how carrier sense works, point out its limitations, and advocate an experimental approach to studying carrier sense. We describe our current testbed setup, and then present preliminary experimental results from both a 60-node sensor network deployment and a small-scale 802.11 deployment. Our preliminary results evaluate how well carrier sense works and expose its limitations.

Data management in the CarTel mobile sensor computing system

We propose a reusable data management system, called CarTel, for querying and collecting data from intermittently connected devices. CarTel provides a simple, incrementally-deployable platform for developing automobile-based sensor applications. Our platform provides a dynamic query system that allows both continuous (standing) and one-shot geo-spatial queries over car position, speed, and sensory data as well as a both a low-cost/high-bandwidth substrate for communicating with a large network of mobile devices.