We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and schedulingalgorithms that have analytically provable performance benefi...
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We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and schedulingalgorithms that have analytically provable performance benefits due to in-network computation as compared to simple data forwarding. To this end, we define a class of functions, the Fully-Multiplexible functions, which includes several functions such as parity, MAX, and kth-order statistics. For such functions we characterize the maximum achievable refresh rate of the network in terms of an underlying graph primitive, the min-mincut. In acyclic wireline networks we show that the maximum refresh rate is achievable by a simple algorithm that is dynamic, distributed, and only dependent on local information. In the case of wireless networks we provide a MaxWeight-like algorithm with dynamic flow-splitting, which is shown to be throughput-optimal.
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