Runtime Control of LoRa Spreading Factor for Campus Shuttle Monitoring

Mu, Di; Chen, Yitian; Shi, Junyang; Sha, Mo

doi:10.1109/icnp49622.2020.9259383

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…This work complements the use of RL techniques in resource allocation, showcasing the potential of intelligent algorithms in optimizing transmission resources. In line with the same objective, Mu et al [49] have introduced a runtime SF allocation scheme that leverages the K-Nearest Neighbors (KNN) algorithm. This work provides further evidence of the importance of considering link characteristics when assigning optimal SFs.…”

Section: A Physical Layermentioning

confidence: 99%

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A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

Maurya,

Hazra,

Kumari

et al. 2024

Preprint

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Section: A Physical Layermentioning

confidence: 99%

“…Data Extraction Rate [70] Resource allocation to avoid collision due to blind transmission. Q-learning Simulation Packet Delivery Rate [49] Optimal selection of SF to maximize the network throughput. K-NN Realtime data Packet Delivery Rate [34] Optimize the SF and emission to improve the network performance.…”

Section: K-mean Simulationmentioning

confidence: 99%