A learning-theory approach to sensor networks

We propose a unified approach to various sensor network applications, using supervised learning. Supervised learning refers to learning from examples, in the form of input-output pairs, by which a system that isn't programmed in advance can estimate an unknown function and predict its values for inputs outside the training set. In particular, we examined random wireless sensor networks, in which nodes are randomly distributed in the region of deployment. When operating normally, nodes communicate and collaborate only with other nearby nodes (within communication range). However, a base station - with a more powerful computer on board - can query a node or group of nodes when necessary and perform data fusion. Learning techniques have been applied in many diverse scenarios. Preliminary research shows that a well-known algorithm from learning theory effectively applies to environmental monitoring, tracking of moving objects and plumes, and localization. We considered some basic concepts of learning theory and how they might address the needs of random wireless sensor networks.