The Jigsaw continuous sensing engine for mobile phone applications

Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the Jigsaw continuous sensing engine, which balances the performance needs of the application and the resource demands of continuous sensing on the phone. Jigsaw comprises a set of sensing pipelines for the accelerometer, microphone and GPS sensors, which are built in a plug and play manner to support: i) resilient accelerometer data processing, which allows inferences to be robust to different phone hardware, orientation and body positions; ii) smart admission control and on-demand processing for the microphone and accelerometer data, which adaptively throttles the depth and sophistication of sensing pipelines when the input data is low quality or uninformative; and iii) adaptive pipeline processing, which judiciously triggers power hungry pipeline stages (e.g., sampling the GPS) taking into account the mobility and behavioral patterns of the user to drive down energy costs. We implement and evaluate Jigsaw on the Nokia N95 and the Apple iPhone, two popular smartphone platforms, to demonstrate its capability to recognize user activities and perform long term GPS tracking in an energy-efficient manner.