DocumentCode :
240019
Title :
Design of energy-aware QoS routing protocol in wireless sensor networks using reinforcement learning
Author :
Jafarzadeh, Sara Zafar ; Moghaddam, Mohammad Hossein Yaghmaee
Author_Institution :
Dept. of Comput. Eng., Ferdowsi Univ., Mashhad, Iran
fYear :
2014
fDate :
4-7 May 2014
Firstpage :
1
Lastpage :
5
Abstract :
Nowadays a major class of wireless sensor networks (WSNs) applications require a minimum quality of service parameters to be satisfied while the wireless sensor nodes are mobile. Most of the standard WSN routing protocols greedily choose the neighbor node with the best quality of service (QoS) parameter(s) as a next hop. However, the data packet might be able to be routed through other neighbors as it might require less QoS. So the energy of the neighbor node with the best QoS will deplete earlier than other nodes which will result in the reduction of network lifetime. Therefore, it is important that QoS routing protocols of WSNs be capable of efficiently balancing energy and other resources consumption throughout the network. In this paper, we proposed EQR-RL, energy-aware QoS routing protocol in WSNs using reinforcement learning. We compare the network performance of our proposed protocol with two other protocols (QoS-AODV and RL-QRP). The packet delivery ratio, average end-to-end delay and impact of the different traffic load on average end-to-end delay are investigated. Simulation results indicate the superiority of our proposed protocol over two others by considering different network traffic load and node mobility in terms of average end-to-end delay and packet delivery ratio.
Keywords :
learning (artificial intelligence); quality of service; routing protocols; telecommunication power management; telecommunication traffic; wireless sensor networks; EQR-RL; QoS-AODV; RL-QRP; WSN routing protocols; average end-to-end delay; data packet; energy-aware QoS routing protocol; packet delivery ratio; reinforcement learning; traffic load; wireless sensor networks; Delays; Learning (artificial intelligence); Quality of service; Routing; Routing protocols; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on
Conference_Location :
Toronto, ON
ISSN :
0840-7789
Print_ISBN :
978-1-4799-3099-9
Type :
conf
DOI :
10.1109/CCECE.2014.6900988
Filename :
6900988
Link To Document :
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