Self Organized Inference of Spatial Structure in Randomly Deployed Sensor Networks PDF Download

Author: Neena A. George
Publisher:
ISBN:
Category :
Languages : en
Pages : 106

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Book Description
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.