TY - GEN
T1 - Performance Evaluation of Machine Learning Based Channel Selection Algorithm Implemented on IoT Sensor Devices and Its Application to Wireless Sensor Network for Building Monitoring System
AU - Hasegawa, So
AU - Kitagawa, Ryoma
AU - Ito, Takumi
AU - Nakajima, Takashi
AU - Kim, Song Ju
AU - Shoji, Yozo
AU - Hasegawa, Mikio
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - The number of IoT devices has dramatically increased. Numerous IoT devices generate enormous traffic, which causes network congestions and packet losses. To deal with network congestions, we have proposed a channel selection algorithm based on reinforcement learning for IoT devices. We have implemented the algorithm on an IoT device with limited function and confirmed autonomous appropriate channel selection in a local area where devices are crowded. In this paper, we conduct evaluation experiments in real environment where devices are deployed distributedly and there are various IoT systems such as Sigfox, LoRaWAN competing for frequency bands. Our experimental results show that the proposed algorithm based on reinforcement learning improves packet delivery rate (frame success rates) and fairness of the network. Furthermore, we apply the machine learning based IoT devices to IoT sensor network for building monitoring. We deploy 30 sensor devices in a 4-floor building and confirm data collection with avoiding congestions and interference by proposed devices.
AB - The number of IoT devices has dramatically increased. Numerous IoT devices generate enormous traffic, which causes network congestions and packet losses. To deal with network congestions, we have proposed a channel selection algorithm based on reinforcement learning for IoT devices. We have implemented the algorithm on an IoT device with limited function and confirmed autonomous appropriate channel selection in a local area where devices are crowded. In this paper, we conduct evaluation experiments in real environment where devices are deployed distributedly and there are various IoT systems such as Sigfox, LoRaWAN competing for frequency bands. Our experimental results show that the proposed algorithm based on reinforcement learning improves packet delivery rate (frame success rates) and fairness of the network. Furthermore, we apply the machine learning based IoT devices to IoT sensor network for building monitoring. We deploy 30 sensor devices in a 4-floor building and confirm data collection with avoiding congestions and interference by proposed devices.
KW - Data Collection
KW - Distributed Channel Selection
KW - IoT
KW - LPWA
KW - Multi-Armed Bandit
KW - Reinforcement Learning
KW - Structure Health Monitoring
UR - http://www.scopus.com/inward/record.url?scp=85084081614&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC48513.2020.9065211
DO - 10.1109/ICAIIC48513.2020.9065211
M3 - Conference contribution
AN - SCOPUS:85084081614
T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
SP - 161
EP - 166
BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Y2 - 19 February 2020 through 21 February 2020
ER -