  We consider a distributed edge computing scenario consisting of several
wireless nodes that are located over an area of interest. Specifically, some of
the "master" nodes are tasked to sense the environment (e.g., by acquiring
images or videos via cameras) and process the corresponding sensory data, while
the other nodes are assigned as "workers" to help the computationally-intensive
processing tasks of the masters. A new tradeoff that has not been previously
explored in the existing literature arises in such a formulation: On one hand,
one wishes to allocate as many master nodes as possible to cover a large area
for accurate monitoring. On the other hand, one also wishes to allocate as many
worker nodes as possible to maximize the computation rate of the sensed data.
It is in the context of this tradeoff that this work is presented. By utilizing
the basic physical layer principles of wireless communication systems, we
formulate and analyze the tradeoff between the coverage and computation
performance of spatial networks. We also present an algorithm to find the
optimal tradeoff and demonstrate its performance through numerical simulations.
