Parallelized Online Regularized Least-Squares for Adaptive Embedded Systems

Introduction

The design of adaptive systems is an emerging topic in the area of pervasive and embedded computing. Rather than exhibiting pre-programmed behavior, it would in many applications be beneficial for systems to be able to adapt to their environment. Isoaho et al. (2010) analyze current key challenges in developing embedded systems. One of the outlined main challenges is self-awareness, meaning that a system should be able to monitor its environment and own state and based on this optimize its behaviour in order to meet service quality criteria. System security is outlined as another major challenge, as is making best use of parallelism that is increasingly present in modern embedded systems.

In order to meet these goals, a system should automatically learn to model its environment in order to choose correct actions, and over time improve its performance as more feedback is gained. Automatically constructed mathematical model of a system may be used to predict the future behaviour, given as input the current state and planned actions, an approach known as model predictive control. The behaviour patterns of software or human agents may be automatically observed in order to recognize possible security risks. And even more, imagine smart music players that adapt to the musical preferences of their owner, intelligent traffic systems that monitor and predict traffic conditions and re-direct cars accordingly, etc. Thus we motivate the need for a generic approach to learning predictive models in embedded environments, which are typically characterized by properties such as need for fast (and constant) response times, limited amount of memory resources and parallel computing architecture.