Abstract-To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions.We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a solar or wind energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts for predictions in both solar-and wind-powered sensor systems increases each system's ability to satisfy its demands compared with existing strategies.
Mobile sensing is difficult without power. Emerging Computational RFIDs (CRFIDs) provide both sensing and generalpurpose computation without batteries-instead relying on small capacitors charged by energy harvesting. CRFIDs have small form factors and consume less energy than traditional sensor motes. However, CRFIDs have yet to see widespread use because of limited autonomy and the propensity for frequent power loss as a result of the necessarily small capacitors that serve as a microcontroller's power supply. Our results show that hybrid harvesting CRFIDs, which use an ambient energy micro-harvester, can complete a variety of useful workloads-even in an environment with little ambient energy available.Our contributions include (1) benchmarks demonstrating that micro-harvesting from ambient energy sources enables greater range and read rate, as well as autonomous operation by hybrid CRFIDs, (2) a measurement study that stresses the limits of effective ambient energy harvesting for diverse workloads, (3) application studies that demonstrate the benefits of hybrid CRFIDs, and (4) a trace-driven simulator to model and evaluate the expected behavior of a CRFID with different capacitor sizes and operating under varying conditions of mobility and solar energy harvesting. Our results show that ambient harvesting can triple the effective communication range of a CRFID, quadruple the read rate, and achieve 95% uptime in RAM retention mode despite long periods of low light.
Conventional wireless network designs to date target endpoint designs that view the channel as a given. Examples include rate and power control at the transmitter, sophisticated receiver decoder designs, and high-performance forward error correction for the data itself. We instead explore whether it is possible to reconfigure the environment itself to facilitate wireless communication. In this work, we instrument the environment with a large array of inexpensive antenna (LAIA) elements, and design algorithms to configure LAIA elements in real time. Our system achieves a high level of programmability through rapid adjustments of an on-board phase shifter in each LAIA element. We design a channel decomposition algorithm to quickly estimate the wireless channel due to the environment alone, which leads us to a process to align the phases of the LAIA elements. We implement and deploy a 36-element LAIA array in a real indoor home environment. Experiments in this setting show that, by reconfiguring the wireless environment, we can achieve a 24% TCP throughput improvement on average and a median improvement of 51.4% in Shannon capacity over baseline single-antenna links.
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