Mobile sensing framework for task partitioning between cloud and edge device for improved performance

Recently smartphones are used every area in day-to-day life. Smartphones comes with several built-in sensors like gyroscope, accelerometer etc., along with powerful processing units. There exist various frameworks which use mobile as sensing device and mobile sensors as data extractor and process extracted data to calculate various parameter. This processing unit can be resided either in mobile side or cloud side, which provides flexibility to the researcher/developer to reduce computation time by migrating processing unit and transferring data to the cloud side. This may create problem of packet dropping or network issue while transferring data to the cloud. To overcome network issue, we propose a common framework which maintains trade-off between network overhead and processing time. The key feature of proposed framework is dividing processing unit into mobile and cloud side, sends raw data to cloud after preprocessing at mobile side. This will take very low processing time and reduce raw data size, which reduces number of packets to send to the cloud. We investigate feasibility of our proposed framework by implementing and testing with several collaborative sensing applications and comparing with the existing framework. Our result shows promising result by trading off between on-board processing and network overhead across all the solutions we had tested.