  Numerous energy harvesting wireless devices that will serve as building
blocks for the Internet of Things (IoT) are currently under development.
However, there is still only limited understanding of the properties of various
energy sources and their impact on energy harvesting adaptive algorithms.
Hence, we focus on characterizing the kinetic (motion) energy that can be
harvested by a wireless node with an IoT form factor and on developing energy
allocation algorithms for such nodes. In this paper, we describe methods for
estimating harvested energy from acceleration traces. To characterize the
energy availability associated with specific human activities (e.g., relaxing,
walking, cycling), we analyze a motion dataset with over 40 participants. Based
on acceleration measurements that we collected for over 200 hours, we study
energy generation processes associated with day-long human routines. We also
briefly summarize our experiments with moving objects. We develop energy
allocation algorithms that take into account practical IoT node design
considerations, and evaluate the algorithms using the collected measurements.
Our observations provide insights into the design of motion energy harvesters,
IoT nodes, and energy harvesting adaptive algorithms.

Emerging Technologies;Networking and Internet Architecture;Performance