Biologically inspired reasoning scheme for semantic sensor network ontology in efficient disaster surveillance

Abstract

Semantic sensor web (SSW) technologies from the sensor web enablement (SWE) standard of open geospatial consortium (OGC) are useful for surveillance in disaster situations. In SSW, the characteristics of sensors are represented as a semantic sensor network (SSN) ontology, which enables semantic interpretation and situation learning. For efficient disaster surveillance, various sensors are deployed over a large-scale geographic area. Furthermore, mobile devices carried by citizens can be recruited during emergencies. However, the heterogeneity of the recruited devices results in the need for additional processing of data attributes. To overcome the shortage of resources during an emergency, a biologically inspired learning scheme can be adopted. The scheme is based on the spike rates of each sensor, thus ignoring much of the information by calculating the relative timing between individual signals shared, and integrated semantic ontologies help deduce information from temporal and spatial contexts. Our approach focuses on the asynchronous and spiking nature of sensors and extracts relevant temporal features in spatial dynamics. We propose a scheme utilizing spike-timing-dependent plasticity (STDP) to process the vast number of signals sent from newly recruited sensors, which factors in the relative timing of signals. To achieve higher reasoning efficiency, mechanisms behind brain synaptic plasticity, specifically, latent inhibition, long-term depression, and long-term potentiation observed in the STDP learning rule are applied. These mechanisms enable a more suitable response inference under time-critical circumstances.