The Tradeoff Between Coverage and Computation in Wireless Networks

TL;DR

This paper explores the balance between coverage and computational efficiency in wireless edge networks, proposing an algorithm to optimize node allocation for improved sensing and processing performance.

Contribution

It introduces a novel analysis of the coverage-computation tradeoff in wireless networks and presents an algorithm to optimize node deployment for this purpose.

Findings

The proposed algorithm effectively balances coverage and computation.

Numerical simulations demonstrate improved network performance.

Tradeoff analysis guides optimal node allocation strategies.

Abstract

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.