Abstract
As a special application scenario, the data collected by wireless sensor networks of coal mine robot is from vital and dangerous environment. Therefore, the nodes need to work as long as possible. In order to efficiently utilize the node energy of wireless sensor network, this paper proposes a self-organizing routing method for wireless sensor networks based on Q-learning. The method takes many factors into account, such as the hop number, distance, residual energy, and node communication loss and energy. Each node of the wireless sensor networks is mapped into an Agent. Periodic training is carried out to optimize the route choice. Each Agent chooses the optimal path for data transmission according to the calculated Q evaluation value. Simulation results show that the self-organizing sensor networks using Q-learning can balance the energy consumption of the nodes and prolong the lifetime of the networks.
Highlights
Coal mine environment is often threatened by toxic gases and high temperature, so coal mine robots [1] often replace human to enter the pit to carry on the detection or the rescue tasks
The simulations are conducted to verify the influence of parameter α on the lifetime of wireless sensor networks, the choice of node paths, the routing energy loss, and the residual energy of nodes
This paper proposes a QLSORP routing algorithm for wireless sensor network in coal mine robot based on Q-learning
Summary
Coal mine environment is often threatened by toxic gases and high temperature, so coal mine robots [1] often replace human to enter the pit to carry on the detection or the rescue tasks. Coal mine robots need to sense the states of themselves, as well as the environment of the underground. Cables for power and signal connections are required, and wiring is a piece of tedious work. Vibration or collision during robots’ motion affects the connection and joint quality of the cable. This will affect the reliability of the data detection and even cause coal mine robots not to work properly in the underground. Different from the general wireless sensor networks (WSNs) [2], a wireless sensor network deployed on a coal mine robot consists of a number of small sensor nodes. This paper studies the energy efficiency, which further improves the energy efficiency and prolongs the network life cycle
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
