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Energy-efficient IoT sensor nodes enable scalable monitoring of diverse physical environments, some of which are exposed to extreme and harsh operating conditions (such as heavy rain or strong movement). For reliable operation of such devices, certain variables must be adaptively adjusted. One of these variables is the transmission power of outgoing packets. In this work, we experimentally investigate how the movement of different bodies of water affects fluctuations in link quality and propose a model for predicting the received power. Once the received power is predicted, a transmitting node can adjust the transmission power to bring the received power to a desired level. Our model is based on minimum mean square estimation (MMSE) and leverages the received power statistics and the movement experienced by the nodes during communication. A disadvantage of MMSE is its dependence on matrix inversion, which is computationally intensive and difficult to implement on resource-constrained devices. We avoid this step and estimate the model parameters using gradient descent, which is much easier to implement. The model achieves an average prediction accuracy of 91% (SD = 1.7%) even with a small number of iterations.
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Timely Remote Estimation with Memory at the Receiver
In this study, we consider a remote estimation system that estimates a time-varying target based on sensor data transmitted over wireless channel. Due to transmission errors, some data packets fail to reach the receiver. To mitigate this, the receiver uses a buffer to store recently received data packets, which allows for more accurate estimation from the incomplete received data. Our research focuses on optimizing the transmission scheduling policy to minimize the estimation error, which is quantified as a function of the age of information vector associated with the buffered packets. Our results show that maintaining a buffer at the receiver results in better estimation performance for non-Markovian sources.
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- Award ID(s):
- 2239677
- PAR ID:
- 10585030
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-5405-8
- Page Range / eLocation ID:
- 111 to 115
- Format(s):
- Medium: X
- Location:
- Pacific Grove, CA, 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/IEEECONF60004.2024.10942803
