Swarm-based semantic fuzzy reasoning for situation awareness computing

Situation awareness computing employs sensor networks to collect large amounts of heterogeneous data in different and complex environments. The rapid development and deployment of sensor technology stress the problem related to the availability of too much and heterogeneous data. Last trend emphasizes the semantic annotation of acquired sensor data. Semantic sensor data provides machine understandable contextual information. In particular, the availability of semantic sensor data allows situation awareness in several application domains. This paper introduces a swarm-based approach to semantic web reasoning in order to identify situations. On one hand, fuzzy control has been employed in order to face with uncertainty of happening situations. On the other hand, Situation Theory has been used in order to model situation awareness. A multi agent swarm architecture enables to monitor complex environments by using spatially distributed autonomous sensors. An application scenario for bank intrusion detection has been described.