ENERGY HARVESTING MICROGENERATORS FOR BODY SENSOR NETWORKS

Abstract

Body sensor networks have the potential to become an asset for personalizing healthcare delivery to patients in need. A key limitation for a successful implementation of body sensor networks comes from the lack of a continuous, reliable power source for the body-mounted sensors. The aim of this thesis is to model and optimize a micro-energy harvesting generator that prolongs the operational lifetime of body sensors and make them more appealing, especially for personalized healthcare purposes. It explores a model that is suitable for harvesting mechanical power generated from human body motions. Adaptive optimization algorithms are used to maximize the amount of power harvested from this model. Practicality considerations discuss the feasibility of optimization and overall effectiveness of implementing the energy harvester model with respect to body sensor power requirements and its operational lifetime.