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SELF ORGANIZED INFERENCE OF SPATIAL STRUCTURE IN RANDOMLY DEPLOYED SENSOR NETWORKS

GEORGE, NEENA A
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163714434

Abstract Details

2006, MS, University of Cincinnati, Engineering : Electrical Engineering.
Randomly deployed wireless sensor networks are becoming increasingly viable for applications such as environmental monitoring, battlefield awareness, tracking and smart environments. Such networks can comprise anywhere from a few hundred to thousands of sensor nodes, and these sizes are likely to grow with advancing technology, making scalability a primary concern. Each node in these sensor networks is a small unit with limited resources and localized sensing and communication. Thus, all global tasks must be accomplished through self-organized distributed algorithms, which also lead to improved scalability, robustness and flexibility. In this thesis, we examine the use of distributed algorithms to infer the spatial structure of an extended environment monitored by a self organizing sensor network. Based on its sensing, the network segments the environment into regions with distinct characteristics, thereby inferring a cognitive map of the environment. This, in turn, can be used to answer global queries about the environment efficiently and accurately. We consider distributed machine learning techniques for segmentation. We also present a heuristic for segmenting within boundaries to obtain distinct segments and study the variation of segmentation quality with reconstruction at different node densities and in environments of varying complexity.
Dr. Ali Minai (Advisor)
106 p.
Self-organization; image processing; inference; sensor networks

Recommended Citations

Citations

  • GEORGE, N. A. (2006). SELF ORGANIZED INFERENCE OF SPATIAL STRUCTURE IN RANDOMLY DEPLOYED SENSOR NETWORKS [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163714434

    APA Style (7th edition)

  • GEORGE, NEENA. SELF ORGANIZED INFERENCE OF SPATIAL STRUCTURE IN RANDOMLY DEPLOYED SENSOR NETWORKS. 2006. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163714434.

    MLA Style (8th edition)

  • GEORGE, NEENA. "SELF ORGANIZED INFERENCE OF SPATIAL STRUCTURE IN RANDOMLY DEPLOYED SENSOR NETWORKS." Master's thesis, University of Cincinnati, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163714434

    Chicago Manual of Style (17th edition)

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© 2006, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.