Internet of Things forms the backbone of modern building applications. Wireless sensors are being increasingly adopted for their flexibility and reduced cost of deployment. However, most wireless sensors are powered by batteries today and large deployments are inhibited by manual battery replacement. Energy harvesting sensors provide an attractive alternative, but they need to provide adequate quality of service to applications given uncertain energy availability. We propose using reinforcement learning to optimize the operation of energy harvesting sensors to maximize sensing quality with available energy. We present our system ACES that uses reinforcement learning for periodic and event-driven sensing indoors with ambient light energy harvesting. Our custom-built board uses a supercapacitor to store energy temporarily, senses light, motion events and relays them using Bluetooth Low Energy.Using simulations and real deployments, we show that our sensor nodes adapt to their lighting conditions and continuously sends measurements and events across nights and weekends. We use deployment data to continually adapt sensing to changing environmental patterns and transfer learning to reduce the training time in real deployments. In our 60 node deployment lasting two weeks, we observe a dead time of 0.1%. The periodic sensors that measure luminosity have a mean sampling period of 90 seconds and the event sensors that detect motion with PIR captured 86% of the events on average compared to a battery powered node.

Supercomputers, nowadays, aggregate a large number of nodes sharing the same nominal HW components (eg. processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e. ACPI p-states) as well as process variations and die binning. Eurora is a fully operational supercomputer prototype that topped July 2013 Green500 and it represents a unique 'living lab' for next-generation ultra-green supercomputers. In this paper we evaluate and quantify the impact of variability on Eurora's energy-performance tradeoffs under a wide range of workload intensity.

As of today, large-scale wireless sensor networks are adopted for smart building applications as they are easy and flexible to deploy. Low-power wireless nodes can achieve multi-year lifetimes with an AA battery using Bluetooth Low Energy (BLE) and Zig-Bee. However, replacing these batteries at scale is a non-trivial, labor-intensive task. Energy harvesting has emerged as a potential solution to avoid battery replacement but requires compromises such as application specific sensor node design, simplified communication protocol or reduced quality of service. We show the design of a battery-free sensor node using commercial off the shelf components, and present Pible: a Perpetual Indoor BLE sensor node that uses an ambient light energy harvesting system and can support numerous smart building applications. We show trade-offs between node-lifetime, quality of service and light availability and present a predictive algorithm that adapts to changing lighting conditions to maximize node lifetime and application quality of service.

Supercomputers, nowadays, aggregate a large number of nodes featuring the same nominal HW components (eg. processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e. ACPI p-states) as well as process variations and die binning. Eurora is a fully operational supercomputer prototype that topped July 2013 Green500 and it represents a unique 'living lab' for next-generation ultragreen supercomputers. In this paper we evaluate and quantify the impact of variability on Eurora's energy-performance tradeoffs under a wide range of workloads intensity. Our experiments demonstrate that variability comes from hardware component mismatches as well as from the interplay between run-time energy management and workload variations. Thus, variability has a significant impact on energy efficiency even at the moderate scale of the Eurora machine, thereby substantiating the critical importance of variability management in future green supercomputers.