An Energy-Efficient Context-Aware Sensor Data Logging for Flash Storage

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Local storage is a fundamental component in many wearable sensing applications. While trade-offs between computation and transmission have been intensively studied, little research exists in optimisation of memory storage resources. Current flash memories offer a cheap, high-capacity and energy-efficient solution, but their potential has not been fully exploited. Novel opportunistic strategies to structure, organise and query compressed data on flash memory are highly desirable for resource-constrained monitoring applications in free-living.

The aim of the project is to design an energy-efficient context-aware sensor data logging system that stores time series in a flash-efficient manner. Prototyping will be done in Python. Evaluation will be performed on real world data set in terms of energy/memory savings, time constraints and quality of information retrieved.

 

Project type Bachelor/Master Thesis
Work distribution 100% Programming
Useful knowledge Python
Starting date Immediate

Contact

Giovanni Schiboni

 

Friedrich-Alexander-Universität Erlangen-Nürnberg