On the use of data inference for energy conservation in wireless sensor networks.
Published: 2004
Language: eng
Number of pages: 84
Description: In this thesis, we propose two approaches to using data inference techniques to conserve energy in wireless sensor networks. First, we propose a novel approach for efficiently sensing a remote field by trading off reduced energy usage for reduced accuracy of the data recorded. Our approach, the  infer algorithm, puts nodes into sleep mode for a given period of time and uses Bayesian inference to infer the missing data from the nodes in sleep mode. Simulations show that on average our algorithm produces energy savings of 59% while producing results that are accurate to within 7.9%. Second, we solve the problem of inferring per node loss rates using passive end-to-end measurements. We formulate the problem as a Maximum-Likelihood Estimation (MLE) problem and show how it can be efficiently solved using the Expectation-Maximization (EM) algorithm. Finally, we validate our analysis through simulations.
Notes: Adviser:  Baochun Li.

Thesis (M.A.Sc.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 43-03, page: 0933.

MICR copy on microfiche (1 microfiche).