Distributed Learning with Kernels in Wireless Sensor Networks for Physical Phenomena Modeling and Tracking

Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. They rely on sensor devices deployed in an environment to support sensing and monitoring, including temperature, humidity, motion, acoustic, etc. Low cost and miniaturization of sensors involve limited computational resources, power and communication capacities. Consequently, wireless ad-hoc sensor networks require collaborative execution of a distributed task on a large set of sensors, with reduced communication and computation burden.