Pattern Recognition in Human Motion for Kinetic Energy Harvesting Kinetic energy harvesting from external vibrations is a common technique to scavenge energy from the environment to power a miniature autonomous sensors. In the first generation of energy harvesters, the designers relied on periodic motion to design and optimize the operation of a harvester. Since the functionality of sensors and the types of environment where they can be placed vary significantly, new techniques to scavenge kinetic energy from irregular motion, in particular the one produced by humans, have emerged. In this paper, we study patterns and selfsimilarity of acceleration time series generated from human motion in application to a technique known as nearlimit kinetic energy harvesters. We show that human motion corresponding to specific physical activities such as running or walking possesses high selfsimilarity and hence is particularly suitable to be used with the nearlimit energy harvesting technique.

Systems that Sustain Themselves: Energy Harvesting Sensor Nodes for Monitoring the Environment Batteryless sensor networks, that harvest energy from the ambient, have attracted much attention in the last few years due to the promise of low maintenance and untethered perpetual operation. However, the major challenge in such networks is that the availability of nodes in the network depends on the energy profile of their harvesting sources. This might affect network reliability. In this work, we study the suitability of Energy Harvesting Sensor (EHS) nodes, powered using light and vibrations, for a simple temperature monitoring application. We evaluate whether such an EHS nodebased system can sustain itself and compare its performance with that of a traditional batterybased system. To economize on energy expenditure in the EHS system, we implement an Autoregressive (AR) model based adaptive sampling algorithm on the EHS nodes. After thorough experimental investigations, we conclude that the EHS nodebased system fares quite well. Results show that adaptive sampling helps achieve energy savings of 62.12% and a 52.33% reduction in the amount of sampled data.