Increasing the precision of mobile sensing sy stems through super-sampling

Sensors integrated into mobile phones have the advantage of mobility, co-location with people, pre-built network and power infrastructure, and potentially, ubiquity. These characteristics, however, also present significant challenges. Mobility means non-uniform sampling in space, and also constrains the size and weight of the sensors. In this paper, we focus on non-uniform sampling, and imprecision. We investigate the question, “Assuming well calibrated sensors, what precision can we expect from a network of sensors embedded in location aware cell phones?” We briefly describe some results that suggest that a Gaussian process based model is appropriate. I. Introduction With increased public focus on environmental conditions and increasing industrialization of developing countries, the need for environmental monitoring has increased significantly. Current air pollution monitoring systems typically consist of highly sensitive, bulky equipment placed in a few strategic locations. These systems, such as the California Air Resource Board (CARB) monitoring system mostly monitor ambient levels over large geographic areas [1]. Not only do systems like CARB have very coarse granularity, but they also only measure the human and environmental health impacts of pollution indirectly. The Networked Suite of Mobile Atmospheric Real-Time Sensors (N-SMARTS) project [2] aims to radically improve the geographic coverage and granularity of environmental monitoring by integrating pollution (and other environmental) sensors into location-aware mobile phones. Our current sensor devices connect to the phone via Bluetooth, and will eventually fit into a modified battery pack, for tight ergonomic integration. Sensors integrated into mobile phones have the advantage of mobility, co-location with people, pre-built network and power infrastructure, and potentially, ubiquity. These characteristics, however, also present significant challenges. Mobility means nonuniform sampling in space, and also constrains the size and weight of the sensors. Although co-location with people means that samples will often be taken near a particular person, hence providing a good approximation of a person’s exposure to pollution, co-location also means that a person’s behavior (putting their phone in their pockets, riding in cars, remaining indoors vs. outdoors) will impact the readings of the sensors. Tracking a person’s location also has enormous