Battery-less Internet of Things (IoT) devices are emerging because it will be environmentally and economically unsustainable to power billions of IoT devices with batteries. Battery-less devices harvest energy from their environment, but the energy supplied by many such sources varies considerably over time, and there is hence a need for buffering surplus energy. This is enabled by the Energy Storage and Voltage Control Unit (ESVU), and the state-of-the-art ESVUs are REACT and CapDYN. We observe that an ideal ESVU (i) wastes minimal energy (efficiency), (ii) enables the application System-on-Chip (SoC) quickly when energy becomes available (responsiveness), (iii) can support a large variety of SoCs (applicability), and (iv) requires few or cheap components and occupies minimal area and volume (overhead). Through detailed circuit-level simulations, we demonstrate that no existing ESVU ticks all the boxes. More specifically, we find that CapDYN and REACT can provide high efficiency and responsiveness, but they fall short in applicability and overhead, respectively, and we thus propose Coulombix to fill this gap. To understand how ESVU design affects performance at the system level, we conduct a case study in which we implement hardware prototypes of REACT and Coulombix and measure the throughput and response time of a diverse set of IoT benchmarks with a solar energy harvester across three seasons. The case study supports the insights of the simulation-based study, while also exposing interesting second-order effects, highlighting that end-to-end analysis is critical when evaluating ESVUs.

The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The key alternative is energy harvesting, but battery-less IoT systems require extensive evaluation to demonstrate that they are sufficiently performant across the full range of expected operating conditions. IoT developers thus need an evaluation platform that (i) ensures that each evaluated application and configuration is exposed to exactly the same energy environment and events, and (ii) provides a detailed account of what the application spends the harvested energy on. We therefore developed the EStacker evaluation platform which (i) enables fair and repeatable evaluation, and (ii) generates energy stacks. Energy stacks break down the total energy consumption of an application across hardware components and application activities, thereby explaining what the application specifically uses energy on. We augment EStacker with the ST-SP optimization which, in our experiments, reduces evaluation time by 6.3× on average while retaining the temporal behavior of the battery-less IoT system (average throughput error of 7.7%) by proportionally scaling time and power. We demonstrate the utility of EStacker through two case studies. In the first case study, we use energy stack profiles to identify a performance problem that, once addressed, improves performance by 3.3×. The second case study focuses on ST-SP, and we use it to explore the design space required to dimension the harvester and energy storage sizes of a smart parking application in roughly one week (7.7 days). Without ST-SP, sweeping this design space would have taken well over one month (41.7 days).

Designing Internet of Things (IoT) devices that solely rely on energy harvesting is the most promising approach towards achieving a scalable and sustainable IoT. The power output of energy harvesters can however vary significantly and maximizing throughput hence requires adapting application behavior to match the harvester's current power output. In this work, we focus on the connection policy of the IoT device and find that the on-demand connect policy - which is used by state-of-the-art IoT runtime systems - and the aggressive maintain connection policy both fall short across a broad range of harvester power outputs. We therefore propose Energy-aware Connection Management (ECM) which tunes the connection policy and sampling frequency to consistently achieve high throughput. ECM accomplishes this by predicting both the average power output of the harvester and the energy consumed by the IoT device with a lightweight analytical model that only requires tracking six energy thresholds. Our evaluation demonstrates that ECM can improve throughput substantially, i.e., by up to 9.5 × and 3.0 × compared to the on-demand connect and maintain connection policies, respectively.

The Internet of Things (IoT) requires ultra-low-power sensor platforms that can be deployed at scale. Scalable systems however cannot be battery-powered because replacing batteries at scale is costly, impractical and has a negative impact on the environment. The key alternative is to rely on energy harvesting, but this is challenging because the developer needs to ensure that the application achieves sufficient throughput, i.e., the sensor platform delivers information to the back-end system at a sufficient rate when provided with a certain amount of energy. We hence propose the analytical Periodic Energy Harvesting Systems (PES) model which enables developers to explore energy versus throughput trade-offs early in the design process – thereby enabling developers to select a reasonable ultralow-power platform and energy harvesting technology before incurring the (significant)) overhead of adapting their application to the particularities of the platform. PES faithfully models the energy consumed by an IoT application during sampling and communication as well as while idle between samples. If the average power output of the energy harvesting subsystem is insufficient to sustain the application, PES uses the mismatch to predict the number of shutdowns required to harvest sufficient energy. PES achieves an average error of 3.6% across the IoT applications we consider in this work; a significant improvement over the 87.3% average error of state-of-the art EH.