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1 Rethinking Data Fusion based Services in Tiered Sensor Networks Karthik Dantu dantu usc edu Abstract Tiered sensor network architectures are gaining currency In contrast with flat networks of impoverished nodes the hitherto common assumption in sensor networking such systems offer the promise of migrating computational load from sensing nodes to higher capability master nodes We argue that for certain data fusion based services this means that compute intensive algorithms often shunned as impractical for sensor networks are in fact a viable possibility Using localization as an example we show how accurate results may be obtained by leveraging this capability without the use of specialized hardware or high configuration detail both of which are standard approaches to the problem when computation is at a premium Specifically we propose a mathematical optimization based framework for localization based on proximity constraints Most variants of localization can be cast into this framework depending on the kinds of input available e g ranging We show accurate results and exploit a technique from distributed optimization to divide the problem into pieces suitable for computation at the master level nodes We conclude with remarks on the general implications of this example for tiered systems with pointers on how it is likely to be applicable to other problems such as power aware routing 1 I I NTRODUCTION Tiered sensor network architectures are a natural platform on which to build compute intensive systems where the computational load is resident on the higher capability master nodes rather than the simpler impoverished sensor nodes Data fusion based services such as localization tracking coverage power aware routing are all computeintensive The Tenet architecture 1 provides a vision of the level of support future sensor network deployments will enjoy The basic idea is to have a large number of moteclass systems providing high density in sensing and a smaller number of



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