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- W4312289380 abstract "With the increasing number of devices connected to the Internet of Things, expectations from its technologies are getting higher. One of the most promising technologies for IoT networks is LoRa. It enables transmission over long distances with minimum power consumption. However, the current solution for network optimization, adaptive data rate, is only able to set the configuration for the stationary environment, where the conditions in the network do not change very often. Adaptive data rate is also not able to deal with mobile nodes. This results in a high number of collisions in a harsh environment, which leads to higher power consumption. Some of the devices have only a very limited power supply and need to prolong the battery lifetime. The latest research has shown that using reinforcement learning techniques, especially algorithms for a multi-armed bandit problem, leads to a better power efficiency and higher packet delivery ratio. In this paper, we briefly introduce energy-wise LoRa@FIIT protocol. We list and briefly describe different algorithms for communication parameters selection and propose a network testbed for simulations. Furthermore, we present results from performed simulations and compare results of two popular multi-armed bandit algorithms, Thompson sampling and upper confidence bound. The results show that the first one provides about 20% higher packet delivery ratio, while the second one provides a slightly lower power consumption." @default.
- W4312289380 created "2023-01-04" @default.
- W4312289380 creator A5054899873 @default.
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- W4312289380 date "2022-01-01" @default.
- W4312289380 modified "2023-09-23" @default.
- W4312289380 title "Optimization of LoRa Networks Using Multi-armed Bandit Algorithms" @default.
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- W4312289380 doi "https://doi.org/10.1007/978-981-19-1653-3_29" @default.
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