Performance Assessment of Reinforcement Learning Policies for Battery Lifetime Extension in Mobile Multi-RAT LPWAN Scenarios
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU145283" target="_blank" >RIV/00216305:26220/22:PU145283 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9854077" target="_blank" >https://ieeexplore.ieee.org/document/9854077</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JIOT.2022.3197834" target="_blank" >10.1109/JIOT.2022.3197834</a>
Alternative languages
Result language
angličtina
Original language name
Performance Assessment of Reinforcement Learning Policies for Battery Lifetime Extension in Mobile Multi-RAT LPWAN Scenarios
Original language description
Considering the dynamically changing nature of the radio propagation environment, the envisioned battery lifetime of the end device (ED) for massive machine-type communication (mMTC) stands for a critical challenge. As the selected radio technology bounds the battery lifetime, the possibility of choosing among several low-power wide-area (LPWAN) technologies integrated at a single ED may dramatically improve its lifetime. In this paper, we propose a novel approach of battery lifetime extension utilizing reinforcement learning (RL) policies. Notably, the system assesses the radio environment conditions and assigns the appropriate rewards to minimize the overall power consumption and increase reliability. To this aim, we carry out extensive propagation and power measurements campaigns at the city-scale level and then utilize these results for composing real-life use-cases for static and mobile deployments. Our numerical results show that RL-based techniques allow for a noticeable increase in EDs' battery lifetime when operating in multi-RAT mode. Furthermore, out of all considered schemes, the performance of the weighted average policy shows the most consistent results for both considered deployments. Specifically, all RL policies can achieve 90% of their maximum gain during the initialization phase for the stationary EDs while utilizing less than 50 messages. Considering the mobile deployment, the improvements in battery lifetime could reach 200%.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/TN01000007" target="_blank" >TN01000007: National Centre for Energy</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Internet of Things Journal
ISSN
2327-4662
e-ISSN
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Volume of the periodical
9
Issue of the periodical within the volume
24
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
25581-25595
UT code for WoS article
000895792600071
EID of the result in the Scopus database
2-s2.0-85136153172