Is data-centric storage and querying scalable?
Proceedings of the 4th international conference on Embedded networked sensor systems - SenSys '06
The scalability of a wireless sensor network has been of interest and importance. We use a constrained optimization framework to derive fundamental scaling laws for 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 or not the increase in energy and storage resources with more nodes is outweighed by the
... weighed by the concomitant application-specific increase in event and query loads. We have figured out the theoretical scaling laws for the networks of 2 dimensional deployment in our previous work  . We report on our work-in-progress aimed at extending the scaling laws to networks of various dimensional deployment. As a recent achievement, we find that m · q 1/2 must be O(N d−1 2d ) for unstructured networks, and m · q d d+1 must be O(N d−1 d ) for structured networks, to ensure scalable network performance, where m is the number of events sensed by a network over a finite period of deployment, q is the number of queries for each event, d is the dimension of deployment, and N is the size of the network. These conditions determine (i) whether or not the energy requirement per node grows without bound with the network size for a fixed-duration deployment, (ii) whether or not there exists a maximum network size that can be operated for a specified duration on a fixed energy budget, and (iii) whether the network lifetime increases or decreases with the size of the network for a fixed energy budget. An interesting finding of this extension is that 3D uniform deployments are inherently more scalable than 2D uniform deployments, which in turn are more scalable than 1D uniform deployments.