4.5 Article

TempMesh - A Flexible Wireless Sensor Network for Monitoring River Temperatures

Journal

ACM TRANSACTIONS ON SENSOR NETWORKS
Volume 19, Issue 1, Pages -

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3542697

Keywords

River temperature monitoring; wireless sensor network; fluvial temperatures; network storage; power efficiency; timestamp alignment; sensor deployment; fish; salmon

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In the Chinook salmon restoration project in the lower Yuba River, a wireless sensor network was designed and deployed to monitor river temperatures. The network successfully collected temperature data for six months, despite inaccuracies in timestamping and flood-induced sensor destruction. The network's low power consumption and low-throughput capabilities proved crucial for ecological sampling.
For a Chinook salmon restoration project in the lower Yuba River in California, we designed and deployed a wireless sensor network to monitor river temperatures at micro-habitat scales. The study required that temperatures be measured along a 3 km study reach, across the channel, and into off-channel areas. To capture diel and seasonal fluctuations, sensors were sampled quarter-hourly for the full duration of the six-month juvenile salmon winter residency. This sampling duration required that nodesminimize power-use. We adopted event-based software on MSP430 micro-controllers with 433 MHz radio and minimized the networking dutycycle. To address link failures, we included network storage. As the network lacked real-time clocks, data were timestamped at the destination. This, coupled with the storage, yielded timestamp inaccuracies, which we re-aligned using a novel algorithm. We collected over six months of temperature data from 35 sensors across seven nodes. Of the packets collected, we identified 21% as being incorrectly timestamped and were able to re-align 41% of these incorrectly timestamped packets. We collected temperature data through major floods, and the network uploaded data until the flood destroyed the sensors. The network met an important need in ecological sampling with ultra-low power (multi-year battery life) and low-throughput.

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