TITLe: Context-Driven Control of Sensors

for Energy Conservation

This research investigates, analyses and addresses the problem of energy conservation in Wireless Sensor Networks (WSNs). It proposes concepts and techniques to extract environmental information that are useful to conserve sensor energy. These concepts and techniques are consolidated in a generic framework we term the CASE: Context Awareness for Sensing Environments framework. The CASE framework is designed for utilising learning and triggering components to conserve energy in both centralised and in-network WSN configurations. Through CASE Compact, an extension framework of CASE, we develop a novel learning algorithm, HiCoRE: Highly Correlated Rules for Energy Conservation, that can autonomously learn and discover highly correlated rules to efficiently regulate sensing operations. Our results demonstrate that our context-aware framework, CASE, provides an effective and efficient way to maximise sensor lifetime in both centralised and in-network operating environments.