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Title Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework
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Publication Date
Date Available
Date Accessioned
Degree MS
Discipline/Department Computer Science
Degree Level masters
University/Publisher Oregon State University
Abstract We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the mobile element moving from one site to another, and different rewards for obtaining data at different sensors. Furthermore, the costs and the rewards are assumed to change abruptly. The goal is to find a "fast" optimal movement pattern/policy of the mobile element that optimizes for the costs and rewards in non-stationary environments. We formulate and solve this problem using a novel optimization framework called Fast Markov Decision Process (FMDP). The proposed FMDP framework extends the classical Markov Decision Process theory by incorporating the notion of mixing time that allows for the trade-off between the optimality and the convergence rate to the optimality of a policy. Theoretical and simulation results are provided to verify the proposed approach.
Subjects/Keywords Sensor Network; Sensor networks
Contributors Nguyen, Thinh P. (advisor); Raich, Raviv (committee member)
Language en
Rights Attribution 3.0 United States
http://creativecommons.org/licenses/by/3.0/us/ [Always confirm rights and permissions with the source record.]
Country of Publication us
Record ID handle:1957/56020
Repository oregonstate
Date Indexed 2017-03-17
Grantor Oregon State University
Issued Date 2015-05-20 00:00:00
Note [] Graduation date: 2015; [peerreview] no;

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