Situation Awareness (SA) concerns all things happening around us and is
typically associated to what is critical with respect to our interest. For
instance, an airplane pilot does not need to be aware of everything around
him but only the information required for driving correctly and safely.
Endsley provided a more formal definition that considers Situation Awareness
as a three-phase process: perception of the elements of an environment in a
given time interval, comprehension of the aforementioned elements and
projection of their states into the next future. More in details, from a
computational viewpoint, SA aims at formalizing and deducing situations
(occurring in the real world) by processing, fusing and abstracting context
We proposed the Perception-Oriented Approach to identify situations. Such
approach is based on two phases: low-level perception and high-level
perception. The low-level perception is realized by means of Semantic
Technologies and allows to generate more abstract information from raw
sensor data by also considering knowledge about the environment. Sensor
Middleware like Global Sensor Network (GSN), Linked Sensor Middleware (LSM),
OpenIoT and Semantic Web Ontologies like W3C SSNO have been employed to
support gathering and processing of sensor data. At this level, we proposed
approaches to assess the reliability, and other quality dimensions, of
sensor data (observations). In particular, we introduced an approach to
exploit a fuzzy-based consensus model to handle cases in which redundant
sensors provide discordant observations.
Moreover, high-level perception is realized by means of techniques like, for
instance, Fuzzy Formal Concept Analysis, Fuzzy Cognitive Maps, etc. and
allows to organize and classify abstract and actionable information to
support the work of DSSs. At this level, we proposed a nature-inspired
approaches, based on Artificial Bees, for mining association rules used to
foster the resilience of the situation identification algorithms and the
adoption of Semantic Web technologies, like SAW Ontology, to model
situations and Semantic Inference Engine (OWL-based and Rule-based) to
reason on them.
Vincenzo Loia received the PhD in Computer Science from the University of Paris VI, France in 1989. From 1989 he is Faculty member at the University of Salerno where he teaches Situational Awareness, IT Project & Service Management. His current position is as Full Professor of Computer Science at Department of Management and Innovation Systems.
He was principal investigator in a number of industrial R&D projects and in academic research projects. He is author of over 350 original research papers in international journals, book chapters, and in international conference proceedings. He edited four research books around agent technology, Internet, and soft computing methodologies. He is founder of the CO.RI.SA. (Research Consortium on Agent System). He is Co-Editor in Chief of Soft Computing, and Founder and Editor in Chief of Ambient Intelligence and Humanized Computing, both published from Springer-Verlag. He serves as Associate Editor in several international Journals, among them IEEE Transaction on Industrial Informatics, IEEE Transaction on Systems, Men, Cybernetics, International Journal on Granular Computing.
He hold in the last years several role in IEEE Society in particular for Computational Intelligence Society (Chair of Emergent Technologies Technical Committee, IEEE CIS European Representative, Vice-Chair of Intelligent Systems Applications Technical Committee).
His current research interests focus on hybrid approaches for situational awareness and cognitive based systems applied in various domains.
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