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Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices.
- Source :
- ACM Transactions on Sensor Networks; May2024, Vol. 20 Issue 3, p1-31, 31p
- Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15504859
- Volume :
- 20
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- ACM Transactions on Sensor Networks
- Publication Type :
- Academic Journal
- Accession number :
- 177375993
- Full Text :
- https://doi.org/10.1145/3649221