Local and Global Knowledge to Improve the Quality of Sensed Data
Source: Cardiff University
Sensor networks are driven by the activities of their deployed environment and they have the potential to use data that has previously been sensed in order to classify current sensed data. In this paper, the authors propose the Knowledge-Based Hierarchical Architecture for Sensing (K-HAS), an architecture for Wireless Sensor Networks (WSNs) that uses different tiers within a network to classify sensed data. K-HAS uses three tiers for in-network classification: the lower tier actively senses the data and packages it with relevant metadata, the middle tier processes the data using a knowledge base of previously classified sensed data and the upper tier provides storage for all data, a global overview of the network and allows users to access, and modify classifications in order to improve future classifications.