  We study the distributed average consensus problem in multi-agent systems
with directed communication links that are subject to quantized information
flow. The goal of distributed average consensus is for the nodes, each
associated with some initial value, to obtain the average (or some value close
to the average) of these initial values. In this paper, we present and analyze
a distributed averaging algorithm which operates exclusively with quantized
values (specifically, the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and rely
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We characterize the
properties of the proposed distributed averaging protocol, illustrate its
operation with an example, and show that its execution, on any timeinvariant
and strongly connected digraph, will allow all agents to reach, in finite time,
a common consensus value that is equal to the quantized average. We conclude
with comparisons against existing quantized average consensus algorithms that
illustrate the performance and potential advantages of the proposed algorithm.
