SenseMyCity: Crowdsourcing an Urban Sensor

TL;DR

SenseMyCity is an extensible smartphone-based urban sensing platform that collects diverse sensor data for city studies, addressing architectural challenges like energy use and connectivity, demonstrated through real-world case studies.

Contribution

This paper introduces a modular, configurable mobile sensing platform that integrates internal and external sensors for urban data collection, with practical deployment and analysis.

Findings

Efficient data collection with low energy consumption.

Flexible support for various internal and external sensors.

Successful case studies on fuel consumption and stress mapping.

Abstract

People treat smartphones as a second skin, having them around nearly 24/7 and constantly interacting with them. Although smartphones are used mainly for personal communication, social networking and web browsing, they have many connectivity capabilities, and are at the same time equipped with a wide range of embedded sensors. Additionally, bluetooth connectivity can be leveraged to collect data from external sensors, greatly extending the sensing capabilities. However, massive data-gathering using smartphones still poses many architectural challenges, such as limited battery and processing power, and possibly connectivity costs. This article describes SenseMyCity (SMC), an Internet of Things mobile urban sensor that is extensible and fully configurable. The platform consists of an app, a backoffice and a frontoffice. The SMC app can collect data from embedded sensors, like GPS, wifi, accelerometer, magnetometer, etc, as well as from external bluetooth sensors, ranging from On-Board Diagnostics gathering data from vehicles, to wearable cardiac sensors. Adding support for new internal or external sensors is straightforward due to the modular architecture. Data transmission to our servers can occur either on-demand or in real-time, while keeping costs down by only using the configured type of Internet connectivity. We discuss our experience implementing the platform and using it to make longitudinal studies with many users. Further, we present results on bandwidth utilization and energy consumption for different sensors and sampling rates. Finally, we show two use cases: mapping fuel consumption and user stress extracted from cardiac sensors.