Machine Learning in Pervasive Computing
Samuel Ο. Idowu, Christer Åhlund, Olov Schelén, Robert Brännström · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2013
Increase in data quantities and number of pervasive systems has resulted in many decision-making systems. Most of these systems employ Machine Learning (ML) in various practical scenarios and applications. Enormous amount of data generated by sensors can be useful in decision-making systems. The rising number of sensor driven pervasive systems presents interesting research areas on how to adapt and apply existing ML techniques effectively to the domain of pervasive computing. In the face of data deluge, ML has proved viable in many application areas such as data mining and self-customizing programs and could bring about great impact in the field of pervasive computing.The objective of this study is to give the underlying concepts of ML techniques that can be applied to problems in the domain of pervasive and mobile computing. The scope of this study covers the three primary types of ML, supervised, unsupervised and reinforcement learning methods. In the process of providing the fundamental knowledge of ML, we present some conceptual terms of ML and the steps required in developing ML system with a great impact on domains outside ML scope.Our findings show that previous works in the area of ubiquitous computing have successfully applied supervised learning and reinforcement learning methods. Hence, this study focuses more on supervised learning and reinforcement learning. In conclusion, we discuss some basic performance evaluation metrics and methods for obtaining reliable classifiers estimates, such as cross-validation and leave-one-out validation.