Building Survivable Software Systems by Automatically Adapting to Sensor Changes

Abstract

Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. If these software systems could automatically adapt to hardware changes, it would significantly reduce the maintenance cost and enable rapid upgrade. In this paper, we study the problem of how to automatically adapt to sensor changes, as an important step towards building such long-lived, survivable software systems. We address challenges in sensor adaptation when a set of sensors are replaced by new sensors. Our approach reconstructs sensor values of replaced sensors by preserving distributions of sensor values before and after the sensor change, thereby not warranting a change in higher-layer software. Compared to existing work, our approach has the following advantages: (a) ability to exploit new sensors without requiring an overlapping period of time between the new sensors and the old ones; (b) ability to provide an estimation of adaptation quality; and (c) ability to scale to a large number of sensors. Experiments on weather data and Unmanned Undersea Vehicle (UUV) data demonstrate that our approach can automatically adapt to sensor changes with 5.7% higher accuracy compared to baseline methods.

Keywords

• sensor adaptation
• sensor change
• survivable software
• machine learning