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1. Heuristic Adaptation for Intermittently Powered Batteryless Sensors

   https://doi.org/10.1145/3551670.3551678  

   Bakar, Abu; Ross, Alexander G.; Sinan Yildirim, Kasim; Hester, Josiah (July 2022, GetMobile: Mobile Computing and Communications)

   Battery-free sensing devices harvest energy from their surrounding environment to perform sensing, computation, and communication. A core challenge for these devices is maintaining usefulness despite erratic, random, or irregular energy availability, which causes inconsistent execution, loss of service, and power failures. Adapting execution (degrading or upgrading) based on available or predicted power/energy seems promising to stave off power failures, meet deadlines, or increase throughput. However, due to constrained resources and limited local information, deciding what and when exactly to adapt is challenging. This article explores the fundamentals of energy-aware adaptation for intermittently powered computers and proposes heuristic adaptation mechanisms to dynamically modulate the program complexity at run-time to enable higher sensor coverage and throughput. While we target battery-free, intermittently powered, resource-constrained sensors, we see a general application to all energy harvesting devices. 

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2. Protean: An Energy-Efficient and Heterogeneous Platform for Adaptive and Hardware-Accelerated Battery-Free Computing

   https://doi.org/10.1145/3560905.3568561  

   Bakar, Abu; Goel, Rishabh; de Winkel, Jasper; Huang, Jason; Ahmed, Saad; Islam, Bashima; Pawełczak, Przemysław; Yıldırım, Kasım Sinan; Hester, Josiah (November 2022, Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (SenSys'22))

   Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines. 

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3. Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices

   https://doi.org/10.1145/3649221  

   Babatunde, Simeon; Alsubhi, Arwa; Hester, Josiah; Sorber, Jacob (May 2024, ACM Transactions on Sensor Networks)

   Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime. 

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4. Reliable Timekeeping for Intermittent Computing

   https://doi.org/10.1145/3373376.3378464  

   de Winkel, Jasper; Delle Donne, Carlo; Yildirim, Kasim Sinan; Pawełczak, Przemysław; Hester, Josiah (March 2020, Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems)

   null (Ed.)

   Energy-harvesting devices have enabled Internet of Things applications that were impossible before. One core challenge of batteryless sensors that operate intermittently is reliable timekeeping. State-of-the-art low-power real-time clocks suffer from long start-up times (order of seconds) and have low timekeeping granularity (tens of milliseconds at best), often not matching timing requirements of devices that experience numerous power outages per second. Our key insight is that time can be inferred by measuring alternative physical phenomena, like the discharge of a simple RC circuit, and that timekeeping energy cost and accuracy can be modulated depending on the run-time requirements. We achieve these goals with a multi-tier timekeeping architecture, named Cascaded Hierarchical Remanence Timekeeper (CHRT), featuring an array of different RC circuits to be used for dynamic timekeeping requirements. The CHRT and its accompanying software interface are embedded into a fresh batteryless wireless sensing platform, called Botoks, capable of tracking time across power failures. Low start-up time (max 5 ms), high resolution (up to 1 ms) and run-time reconfigurability are the key features of our timekeeping platform. We developed two time-sensitive batteryless applications to demonstrate the approach: a bicycle analytics tool, where the CHRT is used to track time between revolutions of a bicycle wheel, and wireless communication, where the CHRT enables radio synchronization between two intermittently-powered sensors. 

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5. An Energy Supervisor Architecture for Energy-Harvesting Applications

   https://doi.org/10.1109/IPSN54338.2022.00033  

   Saoda, Nurani; Wang, Wenpeng; Billah, Md Fazlay; Campbell, Bradford (May 2022, 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN))

   Energy-harvesting designs typically include highly entangled app-lication-level and energy-management subsystems that span both hardware and software. This tight integration makes developing sophisticated energy-harvesting systems challenging, as developers have to consider both embedded system development and intermit-tent energy management simultaneously. Even when successful, solutions are often monolithic, produce suboptimal performance, and require substantial effort to translate to a new design. Instead, we propose a new energy-harvesting power management architecture, Altair that offloads all energy-management operations to the power supply itself while making the power supply programmable. Altair introduces an energy supervisor and a standard interface to enable an abstraction layer between the power supply hardware and the running application, making both replaceable and recon-figurable. To ensure minimal resource conflict on the application processor, while running resource-hungry optimization techniques in the supervisor, we implement the Altair design in a lower power microcontroller that runs in parallel with the application. We also develop a programmable power supply module and a software library for seamless application development with Altair. We evaluate the versatility of the proposed architecture across a spectrum of IoT devices and demonstrate the generality of the plat-form. We also design and implement an online energy-management technique using reinforcement learning on top of the platform and compare the performance against fixed duty-cycle baselines. Results indicate that sensors running the online energy-manager perform similar to continuously powered sensors, have a l0x higher event generation rate than the intermittently powered ones, 1.8-7x higher event detection accuracy, experience 50% fewer power failures, and are 44% more available than the sensors that maintain a constant duty-cycle. 

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