Technical Note: \cal Q-Learning
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Structures for In-Network Moving Object Tracking in Wireless Sensor Networks
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Efficient In-Network Moving Object Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Energy efficient strategies for object tracking in sensor networks: A data mining approach
Journal of Systems and Software
A survey on clustering algorithms for wireless sensor networks
Computer Communications
Adaptive clustering in wireless sensor networks by mining sensor energy data
Computer Communications
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
An Efficient Frequent Patterns Mining Algorithm Based on Apriori Algorithm and the FP-Tree Structure
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
Mining Community Structures in Peer-to-Peer Environments
ICPADS '08 Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems
Journal of Systems and Software
An efficient location tracking structure for wireless sensor networks
Computer Communications
Mining trajectory profiles for discovering user communities
Proceedings of the 2009 International Workshop on Location Based Social Networks
Expert Systems with Applications: An International Journal
Efficient mining of association rules from wireless sensor networks
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
Hi-index | 0.00 |
This paper proposes a new routing algorithm for wireless sensor network. The algorithm uses reinforcement learning and pattern tree adjustment to select the routing path for data transmission. The former uses Q value of each sensor node to reward or punish the node in the transmission path. The factor of Q value includes past transmission path, energy consuming, transmission reword to make the node intelligent. The latter then uses the Q value to real-time change the structure of the pattern tree to increase successful times of data transmission. The pattern tree is constructed according to the fusion history transmission data and fusion benefit. We use frequent pattern mining to build the fusion benefit pattern tree. The experimental results show that the algorithm can improve the data transmission rate by dynamic adjustment the transmission path.