.png){kind=icon}
.png){kind=icon}
Return
Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3265717.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The 6TiSCH (IPv6 over IEEE802.15.4e time-slotted channel hopping mode) wireless sensor network architecture utilizes control packets to construct network formation. These control packets are essential for establishing communication links between nodes and configuring network settings. The trickle timer algorithm is utilized to broadcast the DIO control packet. DIO carries information about the available parent nodes, which is then used to form the routing tree. Sensors transmit control packets in one cell on each TSCH slotframe, called the minimal cell. This leads to the problem that RPL trickle timer algorithm encounters congestion in DIO control packet transmission with other control messages, particularly in dense networks. Moreover, high traffic transmission also leads to high queue usage, which then drops the DIO control packet. Failed DIO transmission can increase network formation time and energy consumption. To address this issue, we propose Q-Trickle, an adaptive trickle timer algorithm based on Q-learning that determines the optimal policy for transmitting or suppressing DIO based on minimal cell and transmission queue conditions. Q-Trickle adaptively selects a redundancy constant value and transmission interval that promotes fair transmission distribution and considers network condition. Additionally, a control scheme over minimal cell transmission is formulated to lower transmission congestion and faster synchronization. The proposed methods were assessed using simulation and actual testing on the FIT IoT-LAB testbed. The results indicated that Q-Trickle performed better than the benchmark methods. Q-Trickle decreases joining time, energy consumption, and number of failed DIO compared with the original algorithm by -13%, -11%, and -43%, respectively.
Keywords:
6TiSCH
MSF
RPL
trickle timer
wireless sensor network
Q-learning