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Báo cáo hóa học: " Research Article Intelligent Broadcasting in Mobile Ad Hoc Networks: Three Classes of Adaptive Protocols"

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 Intelligent Broadcasting in Mobile Ad Hoc Networks: Three Classes of Adaptive Protocols | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2007 Article ID 10216 16 pages doi 10.1155 2007 10216 Research Article Intelligent Broadcasting in Mobile Ad Hoc Networks Three Classes of Adaptive Protocols Michael D. Colagrosso Department of Mathematical and Computer Sciences Colorado School of Mines Golden CO 80401-1887 USA Received 10 February 2006 Revised 3 July 2006 Accepted 16 August 2006 Recommended by Hamid Sadjadpour Because adaptability greatly improves the performance of a broadcast protocol we identify three ways in which machine learning can be applied to broadcasting in a mobile ad hoc network MANET . We chose broadcasting because it functions as a foundation of MANET communication. Unicast multicast and geocast protocols utilize broadcasting as a building block providing important control and route establishment functionality. Therefore any improvements to the process of broadcasting can be immediately realized by higher-level MANET functionality and applications. While efficient broadcast protocols have been proposed no single broadcasting protocol works well in all possible MANET conditions. Furthermore protocols tend to fail catastrophically in severe network environments. Our three classes of adaptive protocols are pure machine learning intra-protocol learning and inter-protocol learning. In the pure machine learning approach we exhibit a new approach to the design of a broadcast protocol the decision of whether to rebroadcast a packet is cast as a classification problem. Each mobile node MN builds a classifier and trains it on data collected from the network environment. Using intra-protocol learning each MN consults a simple machine model for the optimal value of one of its free parameters. Lastly in inter-protocol learning MNs learn to switch between different broadcasting protocols based on network conditions. For each class of learning method we create a prototypical protocol and examine its .