N-smarts: networked suite of mobile atmospheric real-time sensors
Proceedings of the second ACM SIGCOMM workshop on Networked systems for developing regions
Enabling energy efficient continuous sensing on mobile phones with LittleRock
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
FoneAstra: enabling remote monitoring of vaccine cold-chains using commodity mobile phones
Proceedings of the First ACM Symposium on Computing for Development
Hijacking power and bandwidth from the mobile phone's audio interface
Proceedings of the First ACM Symposium on Computing for Development
WebClip: a connector for ubiquitous physical input and output for touch screen devices
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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We present AudioDAQ, a new platform for continuous data acquisition using the headset port of a mobile phone. AudioDAQ differs from existing phone peripheral interfaces by drawing all necessary power from the microphone bias voltage, encoding all data as analog audio, and leveraging the phone's built-in voice memo application (or a custom application) for continuous data collection. These properties make the AudioDAQ design more universal, so it works across a broad range of phones including sophisticated smart phones and simpler feature phones, enables simple analog peripherals without requiring a microcontroller, requires no hardware or software modifications on the phone itself, uses significantly less power than prior approaches, and allows continuous data capture over an extended period of time. The AudioDAQ design is efficient because it draws all necessary power from the microphone bias voltage, and it is general because this voltage and a voice memo application are present on most mobile phones in use today. We show the viability of our architecture by evaluating an end-to-end system that can capture EKG signals continuously for hours and send the data to the cloud for storage, processing, and visualization.