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As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can severely deteriorate, rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, although the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this article first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this article developsDeepIoTRouting, a distributed and scalable deep reinforcement learning (DRL)-based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization,DeepIoTRoutingachieves at least 38.71% improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods.
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Is my sensor sleeping, hibernating, or broken?: A data-driven monitoring system for indoor energy harvesting sensors
As the number of Internet of Things (IoT) devices continues to increase, energy-harvesting (EH) devices eliminate the need to replace batteries or find outlets for sensors in indoor environments. This comes at a cost, however, as these energy-harvesting devices introduce new failure modes not present in traditional IoT devices: extended periods of no harvestable energy cause them to go dormant, their often simple wireless protocols are unreliable, and their limited energy reserves prohibit many diagnostic features. While energy-harvesting sensors promise easy-to-setup and maintenance-free deployments, their limitations hinder robust, long-term data collection. To continuously monitor and maintain a network of energy-harvesting devices in buildings, we propose the EH-HouseKeeper. EH-HouseKeeper is a data-driven system that monitors EH device compliance and predicts healthy signal zones in a building based on the existing gateway location(s) and building profile for easier device maintenance. EH-HouseKeeper does this by first filtering excess event-triggered data points and applying representation learning on building features that describe the path between the gateways and the device. We assessed EH-HouseKeeper by deploying 125 energy-harvesting sensors of varying types in a 17,000 square foot research infrastructure, randomly masking a quarter of the sensors as the test set for validation. The results of our 6-month data-collection period demonstrate an average greater than 80% accuracy in predicting the health status of the subset. Our results validate techniques for assessing sensor health status across device types, for inferring gateway status, and approaches to assist in identifying between gateway, transmission, and sensor faults.
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- Award ID(s):
- 1823325
- PAR ID:
- 10293383
- Date Published:
- Journal Name:
- BuildSys '20: Proceedings of the 7th ACM International Conference on
- Page Range / eLocation ID:
- 210 to 219
- Format(s):
- Medium: X
- 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/10.1145/3408308.3427625
