| description |
The lifetime requirements on wireless sensor networks often require
the redundant deployment of sensor nodes with appropriate management
mechanisms based on node clustering. Yet, existing clustering
approaches do not take the primary task of sensor networks into
account: performing relevant measurements. They usually form
'arbitrary' clusters, e.g., using connectivity
information, and thus, the resulting measurements are often of only
limited use to the applications. This problem can be avoided by
considering application-specific semantics. For indoor applications,
the notion of a room provides a natural unit of clustering since
walls are constructed deliberately to ensure locality. This paper
shows that it is feasible to automatically create clusters that
reflect boundaries between rooms by analyzing the measurements of
inexpensive, broadly available sensors. The paper first analyzes the
applicability of statistical clustering methods and based on this
analysis, it proposes and evaluates a lightweight approach to
determine clusters in real deployments.
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