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Tiny batteryless sensors are desirable since they create negligible impacts on the operation of the system being monitored or the surrounding environment. Wireless energy transfer for batteryless sensors is challenging since they cannot cooperate with the charger due to the lack of energy. In this paper, a Magnetic Blind Beamforming (MagBB) algorithm is developed for wireless energy transfer for batteryless sensors in inhomogeneous media. Batteryless sensors with randomly orientated coils may experience significant orientation losses and they may not receive any energy from the charger. MagBB uses a set of optimized current vectors to generate rotating magnetic fields which can ensure that coils on batteryless sensors with arbitrary orientations can receive sufficient voltages for charging. It does not require any information regarding the batteryless sensor's coil orientation or location. The efficiency of MagBB is proven by extensive numerical simulations.
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Demo Abstract: A Prototype for Machine Learning with Batteryless Sensors
Wearable IoT devices rely on batteries, which pose challenges for long-term sustainable health monitoring due to the need for recharging or replacement. Batteryless sensing approaches, which harvest energy from the environment, offer an appealing alternative. However, given the discontinuous supply of harvested energy, it is unclear how to leverage sparse, asynchronous data from batteryless sensors for machine learning (ML) tasks such as human activity recognition (HAR). To this end, we present and profile a prototype of a system to simulate data acquisition from a set of kinetic energy harvesting devices. Our results demonstrate that there is a need to jointly optimize (1) when sensors should spend energy to communicate data, and (2) the training of the ML model that will receive the data.
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- Award ID(s):
- 2428656
- PAR ID:
- 10599918
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7025-6
- Page Range / eLocation ID:
- 223 to 224
- Subject(s) / Keyword(s):
- Batteryless sensing, human activity recognition
- Format(s):
- Medium: X
- Location:
- Hong Kong, Hong Kong
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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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.more » « less
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Batteryless sensing devices which rely on energy harvesting can enable more sustainable and long-lasting Internet of Things (IoT) based wearables. While it has become feasible to implement energy-harvesting based wearables for digital health applications, it remains challenging to integrate such devices and the data they collect into machine learning pipelines for tasks such as human activity recognition (HAR). A key obstacle is uncertainty in the data acquisition process. Given the discontinuous and uncertain availability of harvested energy, when should a sensor spend energy to sample and transmit data packets for processing? A common approach is to spend energy opportunistically by sending packets whenever sufficient energy is available. However, when considering a specific task, namely HAR with kinetic energy harvesting based sensors, this approach unfairly prioritizes data from activities where more energy can be harvested (e.g., running). In this work, we improve the opportunistic energy spending policy by pruning redundant packets to reallocate energy towards activities where less energy is harvested. Our approach results in an increase in the F1-score of ‘lower energy’ activities while having a minimal impact on the F1-score of ‘higher energy’ activities.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IoTDI61053.2024.00027
