The paper was authored by WiMNet former Ph.D. student Dr. Maria Gorlatova, undergraduate student Mina Cong, Postdoc Dr. Guy Grebla, and Prof. Zussman along with CLUE collaborators Dr. John Sarik and Prof. John Kymissis.
The paper focuses on human and object motion energy availability and properties in commonplace Internet of Things scenarios. It examines the properties of common human motions (walking, running, bicycling) using a 40-participant human motion dataset that was collected in the past for activity recognition purposes. Additionally, it examines kinetic energy availability associated with normal human routines. This examination is based on over 200 hours of acceleration information associated with human daily routines that was collected as part of this study (the collected dataset is available via the CRAWDAD repository). It also demonstrates unexpectedly low energy availability associated with some high-amplitude periodic object motions. Finally, it describes and evaluates energy allocation algorithms for a wearable energy harvesting devices that take into account practical design considerations.