Understanding the Environment through Wireless Sensor Networks

This paper presents a new cognitive architecture for extracting meaningful, high-level information from the environment, starting from the raw data collected by a Wireless Sensor Network. The proposed framework is capable of building rich internal representation of the sensed environment by means of intelligent data processing and correlation. Furthermore, our approach aims at integrating the connectionist, data-driven model with the symbolic one, that uses a high-level knowledge about the domain to drive the environment interpretation. To this aim, the framework exploits the notion of conceptual spaces, adopting a conceptual layer between the subsymbolic one, that processes sensory data, and the symbolic one, that describes the environment by means of a high level language; this intermediate layer plays the key role of anchoring the upper layer symbols. In order to highlight the characteristics of the proposed framework, we also describe a sample application, aiming at monitoring a forest through a Wireless Sensor Network, in order to timely detect the presence of fire.