Average Consensus in the Presence of Delays and Dynamically Changing Directed Graph Topologies

Classical approaches for asymptotic convergence to the global average in a distributed fashion typically assume timely and reliable exchange of information between neighboring components of a given multi-component system. These assumptions are not necessarily valid in practical settings due to varying delays that might affect transmissions at different times, as well as possible changes in the underlying interconnection topology (e.g., due to component mobility). In this work, we propose protocols to overcome these limitations. We first consider a fixed interconnection topology (captured by a - possibly directed - graph) and propose a discrete-time protocol that can reach asymptotic average consensus in a distributed fashion, despite the presence of arbitrary (but bounded) delays in the communication links. The protocol requires that each component has knowledge of the number of its outgoing links (i.e., the number of components to which it sends information). We subsequently extend the protocol to also handle changes in the underlying interconnection topology and describe a variety of rather loose conditions under which the modified protocol allows the components to reach asymptotic average consensus. The proposed algorithms are illustrated via examples.Comment: 37 page

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

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

Control of Quantized Multi-Agent Systems with Linear Nearest Neighbor Rules: A Finite Field Approach

We study the problem of controlling a multi-agent system where each agent is only allowed to be in a discrete and finite set of states. Each agent is capable of updating its state based on the states of its neighbors, and there is a leader agent in the network that is allowed to update its state in arbitrary ways (within the discrete set) in order to put all agents in a desired state. We present a novel solution to this problem by viewing the discrete states of the system as elements of a finite field. Specifically, we develop a theory of structured linear systems over finite fields, and show that such systems will be controllable provided that the size of the finite field is sufficiently large, and that the graph associated with the system satisfies certain properties. We then use these results to show that a multi-agent system with a leader node is controllable via a linear nearest-neighbor update as long as there is a path from the leader to every node, and that the number of discrete states for each node is large enough

On the Time Complexity of Information Dissemination via Linear Iterative Strategies

Given an arbitrary network of interconnected nodes, each with an initial value, we study the number of timesteps required for some (or all) of the nodes to gather all of the initial values via a linear iterative strategy. At each time-step in this strategy, each node in the network transmits a weighted linear combination of its previous transmission and the most recent transmissions of its neighbors. We show that for almost any choice of real-valued weights in the linear iteration (i.e., for all but a set of measure zero), the number of time-steps required for any node to accumulate all of the initial values is upper-bounded by the size of the largest tree in a certain subgraph of the network; we use this fact to show that the linear iterative strategy is time-optimal for information dissemination in certain networks. In the process of deriving our results, we also obtain a characterization of the observability index for a class of linear structured systems