Machine Learning across the WSN Layers

Abstract

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 ef-ficient solutions in environments with distribution and rapidly fluctuating properties, analo-gous 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 net-work stack. First, it provides a brief background and summary of three of the most com-monly used machine learning techniques: reinforcement learning, neural networks and deci-sion trees. Then, it uses example research from the literature to describe current efforts at each level of the stack, and outlines future opportunities. 1. Wireless Sensor Networks Extensive research effort has been invested in recent years to optimize communications in wireless sensor networks (WSNs). Researchers and application developers typically use a communication stack model such as that depicted in Figure 1 to structure the communications of WSNs and to better manage its challenges. In particular, the following properties of WSNs should be considered while designing innovative and efficient solutions (Akyildiz et al., 2002; Römer & Mattern, 2004). • Wireless ad-hoc nature. No fixed communication infrastructure exists. The shared wire-less medium places restrictions on the communication between nodes and poses new problems such as asymmetric links. However, it offers the broadcast advantage: a trans-mitted packet, even if sent in unicast to another node, can be overhead and thus re-ceived by all neighbors of the transmitter.