An Energy-Efficient Context-Aware Sensor Data Logging for Flash Storage
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
Dr. Giovanni Schiboni
- Job title: Researcher
- Phone number: +49 9131 85-23604
- Email: giovanni.schiboni@fau.de