Teaching Networks How To Learn
Decanato - Facoltà di scienze informatiche
Data d'inizio: 14 Maggio 2009
Data di fine: 15 Maggio 2009
Mrs. Anna FÖRSTER, Wednesday May 14th, 2009 at 14:00, room SI-006
The dissertation committee is composed of:
- Prof. Amy L. Murphy, IRST Trento, Italy (advisor)
- Prof. Fernando Pedone, Università della Svizzera italiana, Switzerland (internal member)
- Prof. Luca Maria Gambardella, IDSIA/Università della Svizzera italiana, Switzerland (internal member)
- Prof. Jochen Schiller, Freie Universität Berlin, Germany (external member)
Wireless sensor networks (WSNs) are a fast developing research area with many new exciting applications arising, ranging from micro climate and environmental monitoring through health and structural monitoring to interplanetary communications. At the same time researchers have invested a lot of time and effort into developing high performance energy efficient and reliable communication protocols to meet the growing challenges of WSN applications and deployments.
However, some major problems still remain: for example programming, planning and deploying sensor networks, energy efficient communication, and dependability under harsh environmental conditions.
The main goal of this thesis is to demonstrate that machine learning is a practical approach to a range of complex distributed problems in WSNs.
Showing this will open up new paths for development at all levels of the communication stack. To achieve our goal we contribute a robust, energy-efficient, and flexible data dissemination framework consisting of a routing protocol called FROMS and a clustering protocol called CLIQUE. Both protocols are based on Q-Learning, a reinforcement learning technique, and exhibit vital properties such as robustness against mobility, node and link failures, fast recovery after failures, very low control overhead and a wide variety of supported network scenarios and applications. Both protocols are fully distributed and have minimal communication overhead. Additionally, CLIQUE gives a distributed solution to the recently emerged novel paradigm of non-uniform data dissemination, where the size of the clusters in a network grows with increasing distance from the data sinks.