Renewable Energy Systems
System Support for Green Homes
Solar-Powered Emergency Mesh
Micro-harvestor Driven Gesture Recognition

System Support for Green Homes Our goal is to make it easier for off-grid and grid-tied home residents to make smart choices about managing energy. Renewable technologies, such as solar and wind, are becoming more widely adopted, however, current best practices for energy use and conservation do not necessarily apply in green homes. This project seeks to better understand energy generation and consumption in green homes, and to explore automated techniques for helping residents to achieve better utilization of resources.

Solar-Powered Emergency Mesh

Natural disasters are a common occurrence. How ever hard humans try, we are helpless when it comes to facing the wrath of nature. Earthquakes, floods, and hurricanes are common examples. The aftermaths of such disasters are gargantuan. In addition to human casualties, natural calamities can destroy the power grid, telephone networks, and mobile phone towers leaving survivors stranded without any viable mode of communication. There is no alternative way to disseminate critical information such as disease alerts, safety alerts, location, and direction to survivors.

In this project, we are designing a self-sustainable solar powered mesh that can disseminate critical updates to survivors during post-disaster times. However, designing robust fallback data dissemination meshes during disasters is non-trivial and challenging. First, we treat energy consumption as a fundamental design pillar. We are building a mesh architecture that is energy-efficient, self-sustainable, and available. An operational mesh in the absence of the power grid must rely on renewable energy sources such as solar and should be capable of near-perpetual operation while serving critical updates.

Since renewable energy scavenging is known to be notoriously unpredictable, such a system requires a clean-slate low power hardware and software systems design. We are in the process of developing a system that combines a low-power micro-controller with a higher power microprocessor to provide high availability and computability at minimal energy consumption.

Second, we treat self-healing and self-stabilization as a fundamental property of a fallback mesh architecture. The vagaries and variability of energy scavenging and extreme environmental conditions in the aftermath of a disaster will inevitably lead to permanent and transient node failures. In the event of a failure, nodes in the mesh should automatically redistribute the failed node's data to maintain high levels of redundancy and fault tolerance.

Third, since the primary goal of the mesh is to serve survivors with critical updates, it's design should be general and compatible with off-the-shelf laptops, mobile phones, and PDAs. Hence, we rely on common wireless technology such as Wi-Fi and simple web-based services for information dissemination.

Micro-harvestor Driven Gesture Recognition 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 and individuals with neuromuscular impairments.