Scaling Laws for Data-Centric Storage and Querying in Wireless Sensor Networks

We use a constrained optimization framework to derive scaling laws for data-centric storage and querying in wireless sensor networks. We consider both unstructured sensor networks, which use blind sequential search for querying, and structured sensor networks, which use efficient hash-based querying. We find that the scalability of a sensor network's performance depends upon whether the increase in energy and storage resources with more nodes is outweighed by the concomitant application-specific increase in event and query loads. We derive conditions that determine: 1) whether the energy requirement per node grows without bound with the network size for a fixed-duration deployment, 2) whether there exists a maximum network size that can be operated for a specified duration on a fixed energy budget, and 3) whether the network lifetime increases or decreases with the size of the network for a fixed energy budget. An interesting finding of this work is that three-dimensional (3D) uniform deployments are inherently more scalable than two-dimensional (2D) uniform deployments, which in turn are more scalable than one-dimensional (1D) uniform deployments.