Batteryless sensor nodes, powered solely by energy harvesting, are a promising alternative to battery-powered sensor nodes. However, energy harvesting rates being very low, unreliable, and time-varying, nodes cannot sustain a continuous operation, making them intermittently powered. As a result, these nodes incur unpredictable wakeup times due to continuously varying off-times. To perform tasks like distributed sensing, time synchronization, and communication for intermittently powered nodes, timely execution and robust coordination is vital. To achieve robust coordination, nodes must guarantee to be ON at a coordinated target time regardless of harvesting variations. To ensure robust coordination of on-times, we propose PAIL, a novel hardware-software approach where the hardware component enforces a constant off-time, with small variations. The software component dynamically corrects for those variations. We fabricated a PAIL sensor node and validated its ability to discover neighboring nodes. Using extensive simulations calibrated from experimental measurements, we observe that PAIL maintains nearly 99.3% coordination at steady state. In the context of pairwise communication, we show that leveraging PAIL coordination, nodes improve packet tail latency by 99%.

Batteryless energy-harvesting IoT devices offer maintenance-free and sustainable deployment solutions to enable pervasive intelligence even in remote areas. To develop intelligent and autonomous applications, we need to make decisions locally, often using deep neural networks (DNNs) because communication of sensed data to the cloud is expensive in terms of latency and energy. Further to enable useful applications guaranteeing timeliness while achieving desired performance such as high accuracy is imperative. However, when making decisions locally, guaranteeing timeliness alongside high accuracy is challenging due to the intertwined design of neural network models and harvested energy constraints. In this work, we propose HANNA that uses energy harvesting aware one-shot differentiable Neural Architecture Search (NAS) to produces adaptive DNN inference to maximize accuracy while maintaining timelines. Compared to other state-of-the-art approaches HANNA is able to improve the average inference accuracy by 10% to 44% while reducing the neural network search cost significantly over a range of datasets, latency bounds and harvesting deployment scenario’s.

Wireless sensor nodes powered solely by energy-harvesting show promise in enabling truly pervasive, long-duration sensing by avoiding the fragility, cost, and maintenance limitations of batteries. Unfortunately, since the amount of energy harvested is often significantly less than the active consumption of the device, these devices operate intermittently with limited control of when they are on and how long they are off. Such uncontrollable intermittency first poses a challenge for traditional time synchronization error metrics, since nodes cannot reliably communicate time values at known intervals, resulting in the illusion that nodes are out-of-sync. Second, long duration off-times can exceed the inherent timing limit of persistent clocks that intermittent nodes rely on to measure off-times. Bursty groups of long off-times can cause traditional time synchronization mechanisms to re-converge slowly, incurring significant periods of high error in which nodes are effectively out-of-sync. In this paper, we define the meaning of a shared sense of time for intermittently-powered nodes, and propose two intermittency-aware synchronization error metrics. We then propose an intermittency-resilient time synchronization mechanism, called Levee, that exhibits more rapid re-convergence after losing time and a 2.12× reduction in maximum time synchronization error for a 48-hour period.

Batteryless energy-harvesting sensor nodes can operate indefinitely, but if the harvesting rate is too low, they must operate intermittently. Intermittent operation imposes various challenges upon the system. One of the least-studied is communication–if nodes are unpowered for long, unpredictable periods of time, how can they reliably communicate with each other? In prior work, we proposed the concept of lifecycle management protocols (LMPs) to mitigate this issue and enable wireless communication directly between intermittent sensor nodes using active radios. In this paper, we propose a design framework for a class of LMPs. We then provide analytical models for the delay and throughput of two-node communication using this framework. Finally, we implement this framework on hardware and validate our models in an experimental setting. To the best of our knowledge, this is the first design framework for, and implementation of, protocols for enabling and improving general-purpose communication between intermittent sensor nodes using active radios.

Nodes in batteryless sensor networks operate intermittently, making tasks such as node-to-node communication and coordinated computation extremely challenging. Adding to this challenge, a node typically has little control over its intermittency. Therefore, in this paper, we introduce a new class of protocols, which we call lifecycle management protocols (LMPs), to better control and manage the intermittency of batteryless nodes. These protocols may be designed and optimized for a particular task; here, we propose and evaluate a set of LMPs designed to enable direct communication between intermittent batteryless sensor nodes with active radios.

Batteryless sensing devices powered solely by ambient energy sources are expected to operate in an intermittent manner, since they do not have a predictable, or even continuous, energy supply. When such an intermittent system is powered off, it cannot keep track of time using conventional means. However, a continuous sense of time is critical for any system running real-time or time-sensitive applications. In this paper, we present HARC (Heterogeneous Array of Redundant Persistent Clocks), a novel solution to the problem of timekeeping for batteryless, intermittently-powered systems. HARC uses a heterogeneous, redundant array of capacitor-based persistent clocks that each decay in parallel, but at different rates, to provide variation-resilient high accuracy over a wide range of power off-times. We demonstrate the feasibility and effectiveness of HARC using experimental evaluations on a HARC prototype, and trace-based simulations of HARC-supported communication directly between two devices intermittently-powered by RF harvesting.