Basic Notions of Distributed Detection, Information Fusion and Game Theory

Abstract

Distributed sensor networks consist of a set of spatially distributed sensors that operate as data collectors or decision makers to monitor a shared phenomenon. This is a common case in many real world situations like air-traffic control, economic and finance, medical diagnosis, electric power networks, wireless sensor networks, cognitive radio networks, online reputation systems, and many others. Usually, in centralized networks, if there are no power, channel, communication or privacy constraints, the sensors can send the full raw information they collect to a FC. However, real life situations are different and several constraints must be considered e.g. when sensors are spatially distributed over a large territorial area, when the channel bandwidth is limited, or even when the sensors are supplied with short life power sources. To address these limitations, sensors must perform some local processing before sending a compressed version of the collected information to the FC. The abstraction level of the information summary can vary a lot. For instance, it can be a quantized set of the raw information, a soft summary statistic like an average or a likelihood value, or even a single information bit.