Enforcing situation awareness with granular computing: a systematic overview and new perspectives

Situation Awareness is defined by Endsley as “the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future” and it deals with the continuous extraction of environmental information and its integration with prior knowledge for directing further perception and anticipating future events. To realize systems for Situation Awareness, individual pieces of raw information (e.g. sensor data) should be interpreted into a higher, domain-relevant concept called “situation”, which is an abstract state of affairs interesting to specific applications. The power of using “situations” lies in their ability to provide a simple, human-understandable representation of, for instance, sensor data. The aim of this work is to propose an overview of the applications of Computational Intelligence and Granular Computing for the implementation of systems supporting Situation Awareness. In this scenario, several and heterogeneous Computational Intelligence models and techniques (e.g. Fuzzy Cognitive Maps, Fuzzy Formal Concept Analysis, Dempster–Shafer Theory of Evidence, Ontologies, Knowledge Reasoning, Evolutionary Computing, Intelligent Agents) can be employed to implement such systems. Moreover, in a Situation Identification process, huge volumes of heterogeneous data need processing (e.g. fusion). With respect to this issue, Granular Computing is an information processing theory for using “granules” (e.g. subsets, intervals, fuzzy sets) effectively to build an efficient computational model for dealing with the above-mentioned data. The overview is proposed coherently to both methodological and architectural viewpoints for Situation Awareness