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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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Packet Pruning: Finding Better Energy Spending Policies for Batteryless Human Activity Recognition
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.
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- Award ID(s):
- 2428656
- PAR ID:
- 10599914
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-3014-3
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Location:
- Chicago, IL, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BSN63547.2024.10780463
