Paper Info
Title
Improving Router Cooperation in Mobile Wireless Sensor Networks Using Reinforcement Learning
Abstract
This paper proposes to promote cooperative routing for homogeneous mobile wireless sensor networks (mWSNs) using a scalable, distributed incentive-based mechanism with reasonable resource requirements using reinforcement learning (RL). In particular, Q-learning which is a well-known RL method was integrated an existing Continuous Value Cooperation Protocol (CVCP). We also studied their effects on the efficiency in non-cooperative mWSNs and propose a good routing strategy under constrained conditions such as network traffic load, degree of mobility and path loss exponent.
Year
DOI
Venue
2011
10.1109/EUC.2011.45
EUC
Keywords
Field
DocType
improving router cooperation,network traffic load,incentive-based mechanism,homogeneous mobile wireless,non-cooperative mwsns,mobile wireless sensor networks,well-known rl method,existing continuous value cooperation,cooperative routing,good routing strategy,reinforcement learning,reasonable resource requirement,path loss exponent,mobile wireless sensor network,wireless sensor networks,learning artificial intelligence,protocols,wireless sensor network,mobile computing,path loss
Mobile computing,Homogeneous,Computer science,Mobile wireless,Computer network,Real-time computing,Router,Mobile wireless sensor network,Wireless sensor network,Distributed computing,Scalability,Reinforcement learning
Conference
ISBN
Citations 
PageRank 
978-1-4577-1822-9
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Chanon Rittong100.34
Wipawee Usaha2102.89