Employing Fuzzy Consensus for Assessing Reliability of Sensor Data in Situation Awareness Frameworks

Situation identification is a complex task that is usually employed in order to sustain the work of Decision Support Systems in several and heterogeneous application scenarios like, for instance, Emergency Management, Safety and Security. Typically, situation awareness systems gather and process raw sensor data by means of different techniques. In this context, it is fundamental to exploit qualitative sensor data in order to guarantee the reliability of the situation identification task results. The consolidation of Internet of Things and the growth of the Linked Sensor Data ecosystem provide us with different degrees of availability and, sometimes, redundancy of sensor observations that could be conflicting. This could be caused by sensor failures due to contextual factors, malicious attacks, faults. This paper proposes an approach based on Fuzzy Consensus to assess data coming from a group of redundant sensors and provide reliable observations to be exploited for situation identification. Lastly, Granular Computing paradigm is adopted to handle multi-granularity of information, i.e., to manage observations assessed in different linguistic term sets.