QL-MAC: A Q-Learning Based MAC for Wireless Sensor Networks

2013 ◽  
pp. 267-275 ◽  
Author(s):  
Stefano Galzarano ◽  
Antonio Liotta ◽  
Giancarlo Fortino
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Q Learning

Q-learning based routing for in-network aggregation in wireless sensor networks

2021 ◽  
Vol 27 (3) ◽  
pp. 2231-2250
Author(s):  
Radhakrishnan Maivizhi ◽  
Palanichamy Yogesh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Q Learning ◽  
Network Aggregation

Q-Learning-Based Optimized Routing in Biomedical Wireless Sensor Networks

2016 ◽  
Vol 63 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Jose Anand ◽  
J. Raja Paul Perinbam ◽  
D. Meganathan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Q Learning

A Neural-Q-Learning Based Approach for Delay Tolerant Underwater Acoustic Wireless Sensor Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1530-1535
Author(s):  
Lin Zou ◽  
De Feng Huang ◽  
Roberto Togneri
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Underwater Acoustic Communications ◽  
Frequency Channel ◽  
Underwater Acoustic ◽  
Continuous Space ◽  
Q Learning ◽  
Delay Tolerant

Delay tolerance is a major design concern for supporting underwater acoustic wireless sensor networks (UA-WSNs) to carry out tasks in harsh subsea environments. Due to the great difference between the underwater acoustic channel and the radio frequency channel, most of the existing routing protocols developed for terrestrial wireless sensor networks perform poorly in underwater acoustic communications. In this work, we present a Neural-Q-Learning algorithm based delay tolerant protocol for UA-WSNs. Due to the advantages of the artificial neural network along with the Q-Learning algorithm, the ferry node is capable of determining an optimal route in a two-dimensional continuous space to relay packets effectively and efficiently between sensors. Simulation results show that the delivery delay and delivery cost of the network significantly decrease by maximizing the meeting probability between the ferry node and sensors.

Hardware-Based Model of Node Clustering Using Q-Learning for Wireless Sensor Networks

2018 ◽  
Author(s):  
Gienel Francheska Delos Santos Manarang ◽  
Rusty John Lloyd Rodriguez Mina ◽  
Mikko Chino Reyes Salvador ◽  
Maria Theresa Gusad De Leon ◽  
Chris Vincent Jagunap Densing ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Q Learning ◽  
Node Clustering

A Cooperative Routing Protocol Based on Q-Learning for Underwater Optical-Acoustic Hybrid Wireless Sensor Networks

2021 ◽  
pp. 1-1
Author(s):  
Zhongwei Shen ◽  
Hongxi Yin ◽  
Lianyou Jing ◽  
Yanjun Liang ◽  
Jianying Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Wireless Sensor ◽  
Q Learning ◽  
Cooperative Routing
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