Terracorder: Sense Long and Prosper

TL;DR

This work introduces Terracorder -- a versatile multi-sensor device -- and showcases its exceptionally low power consumption using an on-device reinforcement learning scheduler, and explores how a collaborative scheduler can maximise the useful operation of a network of devices, improving overall network power consumption and robustness.

Abstract

In-situ sensing devices need to be deployed in remote environments for long periods of time; minimizing their power consumption is vital for maximising both their operational lifetime and coverage. We introduce Terracorder -- a versatile multi-sensor device -- and showcase its exceptionally low power consumption using an on-device reinforcement learning scheduler. We prototype a unique device setup for biodiversity monitoring and compare its battery life using our scheduler against a number of fixed schedules; the scheduler captures more than 80% of events at less than 50% of the number of activations of the best-performing fixed schedule. We then explore how a collaborative scheduler can maximise the useful operation of a network of devices, improving overall network power consumption and robustness.

Figures (4)

• Figure 1: Prototype configuration
• Figure 1: PowerFeather current measurements (3.35V in)
• Figure 2: Q-learning hyperparameters
• Figure 2: Performance of fixed vs. Q-learning schedules.