Poster Abstract: Towards a Predictive Model for Improved Placement of Solar-Powered Urban Sensing Nodes

In a world driven by data, cities are increasingly interested in deploying networks of smart city devices for urban and environmental monitoring. To be successful, these networks must be reliable, low-cost, and easy to install and maintain—criteria that are all significantly affected by the design choices around power and can seemingly be satisfied with the use of solar energy. However, solar power is not ubiquitous throughout cities, making it difficult to know where to place nodes to avoid charging issues and thus potentially increasing maintenance costs. This abstract describes the development of a machine learning model that predicts whether any arbitrary location in a city will have solar charging issues. Using data from a large-scale real-world solar-powered sensor deployment in Chicago, Illinois and open data about building location and height, the binary classification model outputs the probability of adequate solar charging at a node location with 77% accuracy on the held-out test set. This work lays the foundation for those deploying future solar-powered urban sensor networks to have more confidence in the reliability of their chosen node locations.

I. Introduction

As the global urban population continues to grow, cities are increasingly interested in monitoring urban processes such as vehicular traffic, and environmental harms including air pollution and noise, to help cities grow in a healthy and sustainable fashion [1], [2]. The lowering cost of sensing infrastructure has encouraged city officials, researchers, and urban residents to use large-scale, low-cost sensor networks to collect data, monitor hyperlocal phenomena, and inform policy to help transition to becoming smart cities [2], [3].