Title: K-HAS: AN ARCHITECTURE FOR USING LOCAL AND GLOBAL KNOWLEDGE IN WIRELESS SENSOR NETWORKS

Year of Publication: Jun - 2012
Page Numbers: 331-341
Authors: Christopher Gwilliams, Alun Preece, Alex Hardisty, Benoit Goossens, Laurentius N. Ambu
Conference Name: The International Conference on Informatics and Applications (ICIA2012)
- Malaysia

Abstract:


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 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 classifcation: the lower tier actively 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 classifcations in order to improve future classifcations. Initial experiments 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 Sanctuary, Malaysia.