|InterJournal Complex Systems, 1825
|Manuscript Number: |
Submission Date: 2006
|Self-organized inference of spatial structure by randomly deployed sensor networks|
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 leads to improved scalability, robustness and flexibility. In this paper, 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, is used to answer global queries about the environment efficiently and accurately. The main challenge to the network arises from the necessarily irregular spatial sampling and the need for totally distributed computation. We consider distributed machine learning techniques for segmentation and study the variation of segmentation quality with reconstruction at different node densities and in environments of varying complexity. The eventual goal of the work presented in this paper is to obtain intelligent networks capable of autonomous reconfiguration based on their observations. The inference of spatial structure in monitored environments is clearly an essential first step for such self-reconfigurability.
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