Local and global knowledge to improve the quality of sensed data
Christopher Gwilliams, Alun D. Preece, Alex Hardisty · ORCA Online Research @Cardiff (Cardiff University) · 2012
Wireless sensor networks (WSNs) allow for wireless communications between embedded devices that can be deployed for long periods in harsh environments. Because of their flexibility, WSNs are applicable to a wide variety of domains and a lot of research has been done to determine the best topology for a network, or the best routing protocol. Much of this research is aimed to solve a specific problem and can be difficult to translate to other scenarios. There is already substantial research on the the various routing protocols that have been developed for WSNs. [3] and [2] survey routing protocols highlighting the constraints of deploying a WSN, such as battery life, transmission medium or coverage, and how each protocol addresses changes in the topology of a network as well as aiming to be as energy efficient as possible. In this paper, we explore the higher level architecture of a sensor network and propose the Knowledge Based Hierarchical Architecture for Sensing (K-HAS), an architecture designed to utilise the knowledge related to its environment in order to classify sensed data. We define sensed data as data that originates from a node that is related to what that node has been tasked to sense. There has been research into sensor networks that use context-awareness in order to improve the quality of the sensed data, as well as the lifetime of the network. In [25], sensors have been used to monitor the movements of patients and adapt their power usage based on the behaviour of the patient. K-HAS aims to extend context-awareness in order to use the knowledge of its environment to classify the sensed data. We call a sensor’s knowledge of its environment local knowledge, Local and Global Knowledge to Improve the Quality of Sensed Data