Sensor-based clustering for indoor applications

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.