The employment of sophisticated tools for data analysis in distributed or structurally complex systems requires the development of specific architectures and data fusion strategies in order to integrate heterogeneous information coming from the environmental sensors. Recently, intelligent sensor networks have been widely deployed for various purposes concerning both security- and safety-oriented systems. Military and civil applications ranging from border surveillance and public spaces monitoring to ambient intelligence and road safety are examples of such various applications. The architecture presented in this article is based on the Cognitive Node (CN) — a module able to receive data from the sensors, process it in order to find potentially harmful or anomalous events and situations and, in some cases, to interact with the environment itself or contact the human operator. The cognitive model was studied and exploited, focusing on the analysis and decision blocks which represent the crucial phases for assessing potentially unsecure/unsafe events and/or situations. The scalability of the model with regards to different application domains was investigated during the research activity. Proposed results show the capability of the given architecture for analysis and assessment of the occurring interactions, with the goal of maintaining proper security/safety levels in the monitored environment.
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