Rethinking Quality Metrics for Low-Cost Urban Environmental Sensor Networks

Abstract

Urban residents can be exposed to environmental harms that can contribute to chronic illness and/or a decreased quality of life. These harms are currently most typically measured by geographically sparse regulatory monitors, making it difficult to determine the areas and residents most affected given the fine-grained heterogeneity of these harms. Simulation and modelling are frequently used to address this issue, but these approaches result in residual uncertainty due to simplifications and estimates in proxies used (such as omitting highway elevations and making assumptions about pollutants in residential versus industrial areas). Dense low-cost sensor network deployments can provide localized data to improve citywide environmental monitoring models, identify urban hotspots, and promote environmental justice. However, the successful leveraging of these networks is dependent on strategically selecting where to place sensor nodes, a step that is often skipped in urban deployments and remains an open question for network designers. Knowing how to strategically place sensors requires a metric against which to measure success.

In Computer Science, node placement strategies often focus on optimizing for area coverage, but this does not accommodate for the three-dimensionality or social nature of cities. This thesis attacks this challenge via the development of quality metrics that account for the three-dimensional nature of cities, using open data to estimate how well nodes are distributed based on different parameters. The primary goal is to develop metrics that focus on key aspects needed for successful network deployments based on the constraints, criteria, sensor placement locations, and design strategies used in prior real-world urban sensor network deployments. The findings from these deployments point to three main themes for new quality metrics that are explored in this thesis: network reliability, "representativeness", and social equity.

This thesis first presents quality metrics for network reliability, using data from a low-cost, large-scale urban sensor network deployment to build models that predict connectivity and power issues at single node locations. The best performing models have 75% accuracy and 77% accuracy for connectivity and power, respectively, showing promise for a metric that can accurately predict locations without reliability issues. The thesis next lays the foundation for a quality metric that estimates "data representativeness", or sensor reading utility, by incorporating data from a large-scale urban sensor network, field work experiments, and open data about urban form and land use. The findings show that sensors on certain road types can potentially provide useful data up to 750 meters away but other road types may need more dense sensor placement to account for variability. Finally, to determine how well sensor networks can be designed to achieve social equity goals, the thesis examines differences in commonly used inequality metrics by combining US census data with simulated node placements. The results show that inequality metrics based on the minimum distance to a sensor node are not well suited for urban sensor network design because they show similar levels of inequality regardless of the number of sensors deployed. Thus, new metrics or parameters must be used to evaluate sensor networks for equity goals.

This thesis asks the critical question of how low-cost urban environmental sensor networks should be designed and evaluated to determine whether they are well designed for the people they aim to serve. Through the development of novel analyses and proposed quality metrics, this work highlights the power of using open data sources about various urban features in laying the foundation to answer that question. By combining open data with findings from field experiments and a real-world sensor network deployment, methods and findings are provided to allow network designers, city employees, and residents to consider and evaluate network designs in new ways. Thus this thesis will aid in the deployment of long-term, reliable, equitable urban environmental sensor networks, contributing to the future development of smart, healthy, sustainable cities.