ioTree: a battery-free wearable system with biocompatible sensors for continuous tree health monitoring

Dang, Tuan, Tran, Trung, Nguyen, Khang, Pham, Tien, Pham, Nhat, Vu, Tam and Nguyen, Phuc 2022. ioTree: a battery-free wearable system with biocompatible sensors for continuous tree health monitoring. Presented at: ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking, Sydney, Australia, 17- 21 October 2022. MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. Association for Computing Machinery, pp. 769-771. 10.1145/3495243.3558749

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Abstract

We present a low-maintenance, wind-powered, battery-free, biocompatible, tree wearable, and intelligent sensing system, namely IoTree, to monitor water and nutrient levels inside a living tree. IoTree system includes tiny-size, biocompatible, and implantable sensors that continuously measure the impedance variations inside the living tree's xylem, where water and nutrients are transported from the root to the upper parts. The collected data are then compressed and transmitted to a base station located at up to 1.8 kilometers (approximately 1.1 miles) away. The entire IoTree system is powered by wind energy and controlled by an adaptive computing technique called block-based intermittent computing, ensuring the forward progress and data consistency under intermittent power and allowing the firmware to execute with the most optimal memory and energy usage. We prototype IoTree that opportunistically performs sensing, data compression, and long-range communication tasks without batteries. During in-lab experiments, IoTree also obtains the accuracy of 91.08% and 90.51% in measuring 10 levels of nutrients, NH3 and K2O, respectively. While tested with Burkwood Viburnum and White Bird trees in the indoor environment, IoTree data strongly correlated with multiple watering and fertilizing events. We also deployed IoTree on a grapevine farm for 30 days, and the system is able to provide sufficient measurements every day.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	978-1-4503-9181-8
Date of First Compliant Deposit:	14 August 2023
Date of Acceptance:	1 October 2022
Last Modified:	31 Aug 2023 16:15
URI:	https://orca.cardiff.ac.uk/id/eprint/161747

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