Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Reinforcement Learning Based Framework for Prediction of Near Likely Nodes in Data-Centric Mobile Wireless Networks | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010 Article ID319275 17 pages doi 2010 319275 Research Article A Reinforcement Learning Based Framework for Prediction of Near Likely Nodes in Data-Centric Mobile Wireless Networks Yingying Chen 1 Hui Wendy Wang 2 Xiuyuan Zheng 1 and Jie Yang1 1 Department of Electrical Engineering Stevens Institute of Technology Hoboken NJ 07030 USA 2 Department of Computer Science Stevens Institute of Technology Hoboken NJ 07030 USA Correspondence should be addressed to Yingying Chen Received 5 September 2009 Revised 8 May 2010 Accepted 10 June 2010 Academic Editor Sayandev Mukherjee Copyright 2010 Yingying Chen et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Data-centric storage provides energy-efficient data dissemination and organization for the increasing amount of wireless data. One of the approaches in data-centric storage is that the nodes that collected data will transfer their data to other neighboring nodes that store the similar type of data. However when the nodes are mobile type-based data distribution alone cannot provide robust data storage and retrieval since the nodes that store similar types may move far away and cannot be easily reachable in the future. In order to minimize the communication overhead and achieve efficient data retrieval in mobile environments we propose a reinforcement learning-based framework called PARIS which utilizes past node trajectory information to predict the near likely nodes in the future as the best content distributee. Our framework can adaptively improve the prediction accuracy by using the reinforcement learning technique. Our experiments demonstrate that our approach can effectively and efficiently predict the future neighborhood. 1.
