Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation

TL;DR

This paper introduces a distributed averaging algorithm for multi-agent systems with directed, quantized communication links, using event-triggered updates to achieve finite-time consensus on the exact average value.

Contribution

It proposes a novel quantized, event-driven consensus algorithm that guarantees finite-time convergence to the exact average in directed networks.

Findings

The algorithm achieves finite-time consensus on the exact average.

It operates effectively on any strongly connected directed graph.

Event-triggered updates reduce communication and energy usage.

Abstract

We study distributed average consensus problems 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 relies 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 on quantized values and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value represented as the ratio of two integer that is equal to the exact average. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.