K-HAS: An Architecture for Using Local and Global Knowledge in Wireless Sensor Networks

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, we propose the Knowledge-Based Hier-archical 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 ac-tively senses the data and packages it with relevant meta-data, the middle tier processes the data using a knowledge base of previously classified sensed data and the the upper tier provides storage for all data, a global overview of the network and allows users to access, and modify classifica-tions in order to improve future classifications. Initial ex-periments on the performance of the individual components of K-HAS have proven successful and a prototype network is planned for deployment in the Kinabatangan Wildlife Sanc-tuary, Malaysia. 1