Evaluating and debugging solar panel-driven systems is a cumbersome process. The system must be deployed in the wild, software and hardware bugs identified remotely, and the development cycle must be repeated to build a robust system. Emulation platforms for solar panels offer a plausible remedy, however, existing systems have narrow operating range and are not portable. To address these shortcomings, we design, implement, and evaluate the SunaPlayer, a solar panel emulation platform that supports a wide operating current range (430 micro-amps to 1.89 amps) and voltage range (0.02 V to 9.8 V), and can be powered using batteries. SunaPlayer uses a high gain analog device, a PNP darlington transistor, a multi-scale driving and measurement circuit, and a novel state machine based proportional-integral-differential controller to build an accurate non-linear model of the solar panel. We have implemented a fully functional SunaPlayer prototype, and demonstrate that it can emulate a wide range of solar panels with high accuracy, low latency, high sensitivity, and low power consumption. The design methodology for the SunaPlayer can be used to build systems that model hard to emulate non-linear devices.

Micro-harvesting from sources such as indoor light can enable a plethora of self-sustainable sensing systems for mobile healthcare applications. However, given the minuscule and variable amount of energy harvested from these renewable sources, practical sensing systems powered by micro-harvesting is today limited to light driven motion sensing. In this paper, we design, implement, and evaluate an indoor light driven wearable glove device that uses flex sensors and accelerometers for hand gesture recognition. Through the design, we make a two-fold contribution to micro-harvester driven mobile sensing systems. First, motivated by extensive profiling of panels for indoor light scavenging, we design a harvester that multiplexes panels of different compositions to maximally scavenge energy as a function of lighting conditions. Second, we present a tiered architecture composed of application specific hardware logic, wakeup controllers, a general purpose micro-controller, and a bluetooth device that can adapt to variable and ultra-low energy constraints, and at the same time provide high responsiveness and compute capability for gesture recognition. We evaluate the glove device in the context of a hand gesture driven home automation system for the elderly.