Machine Learning across the WSN Layers
Anna Förster, Amy L. · InTech eBooks · 2011
Wireless sensor networks (WSNs) have seen rapid research and industrial development in recent years.Both the costs and size of individual nodes have been constantly decreasing, opening new opportunities for a wide range of applications.Nevertheless, designing software to achieve energy-efficient, robust and flexible data dissemination remains an open problem with many competing solutions.In parallel, researchers have effectively exploited machine learning techniques to achieve efficient solutions in environments with distribution and rapidly fluctuating properties, analogous to WSN domains.Applying machine learning techniques to WSNs inherently has the potential to improve the robustness and flexibility of communications and data processing, while simultaneously optimizing energy expenditure.This chapter concentrates on applications of machine learning at all layers in the WSN network stack.First, it provides a brief background and summary of three of the most commonly used machine learning techniques: reinforcement learning, neural networks and decision trees.Then, it uses example research from the literature to describe current efforts at each level of the stack, and outlines future opportunities. How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: Anna Forster and Amy L.