Abstract: Wireless sensors and edge-cloud analytics have the potential to gather and process vast amounts of data about the physical world, offering radical new insights about everything from critical infrastructure to interpersonal interactions. But designing, deploying, and operating geographically-distributed systems consisting a hierarchy of sensing, storage, compute, and communication elements raises interesting new challenges across the system stack. In this talk, we will discuss our experiences designing new IoT systems to address several power and power grid monitoring problems. In particular, this talk will focus on three systems—PowerBlade, Triumvi, and GridWatch—and their motivation, design, and deployment. PowerBlade explores how to cost-effectively characterize, capture, and classify widespread plug-load energy usage—representing the fastest growing and least understood segment of end-use energy consumption—across hundreds of homes and offices representing tens of thousands of sensors.
Triumvi explores how to make circuit level energy metering, useful for a variety of facilities trending, energy savings, and fault detection & diagnostics applications, more efficient and scalable. Finally, GridWatch explores how to scalably and cost-effectively detect and respond to the power outages that stymie residential and business activity in under-developed power grids using mobile and fixed sensors, data analytics, and reporting systems in Sub-Saharan Africa, finding that conventional approaches to outage detection systems vastly underreport customer experiences. These systems all share a similar architecture, require new sensor devices and edge-cloud data processing, and wrestle with power management and networking. But they ultimately demonstrate both the tremendous potential and the significant challenges of this nascent computing class.