A general discrete-time method to achieve resilience in consensus algorithms

In this paper, we approach the problem of a set of network agents reaching resilient consensus in the presence of a subset of attacked nodes. We devise a generalized method, with polynomial time complexity, which receives as input a discrete-time, synchronous-communication consensus algorithm, a dynamic network of agents, and the maximum number of attacked nodes. The distributed algorithm enables each normal node to detect and discard the values of the attacked agents while reaching the consensus of normal agents for the input consensus algorithm. Hence, the proposed method adds an extra layer of resilience to a given discrete-time and synchronous-communication consensus algorithm. Finally, we demonstrate the effectiveness of the method with experimental results, showing some attack circumstances which we can counter, where the state-of-the-art methods fail

Information Propagation Algorithms for Consensus Formation in Decentralized Multi-Agent Systems

Consensus occurs within a multi-agent system when every agent is in agreement about the value of some particular state. For example, the color of an LED, the position or magnitude of a vector, a rendezvous location, the most recent state of data within a database, or the identity of a leader are all states that agents might need to agree on in order to execute their tasking. The task of the decentralized consensus problem for multi-agent systems is to design an algorithm that enables agents to communicate and exchange information such that, in finite time, agents are able to form a consensus without the use of a centralized control mechanism. The primary goal of this research is to introduce and provide supporting evidence for Stochastic Local Observation/Gossip (SLOG) algorithms as a new class of solutions to the decentralized consensus problem for multi-agent systems that lack a centralized controller, with the additional constraints that agents act asynchronously, information is discrete, and all consensus options are equally preferable to all agents. Examples of where these constraints might apply include the spread of social norms and conventions in artificial populations, rendezvous among a set of specific locations, and task assignment. This goal is achieved through a combination of theory and experimentation. Information propagation process and an information propagation algorithm are derived by unifying the general structure of multiple existing solutions to the decentralized consensus problem. They are then used to define two classes of algorithms that spread information across a network and solve the decentralized consensus problem: buffered gossip algorithms and local observation algorithms. Buffered gossip algorithms generalize the behavior of many push-based solutions to the decentralized consensus problem. Local observation algorithms generalize the behavior of many pull-based solutions to the decentralized consensus problem. In the language of object oriented design, buffered gossip algorithms and local observation algorithms are abstract classes; information propagation processes are interfaces. SLOG algorithms combine the transmission mechanisms of buffered gossip algorithms and local observation algorithms into a single hybrid algorithm that is able to push and pull information within the local neighborhood. A common mathematical framework is constructed and used to determine the conditions under which each of these algorithms are guaranteed to produce a consensus, and thus solve the decentralized consensus problem. Finally, a series of simulation experiments are conducted to study the performance of SLOG algorithms. These experiments compare the average speed of consensus formation between buffered gossip algorithms, local observation algorithms, and SLOG algorithms over four distinct network topologies. Beyond the introduction of the SLOG algorithm, this research also contributes to the existing literature on the decentralized consensus problem by: specifying a theoretical framework that can be used to explore the consensus behavior of push-based and pull-based information propagation algorithms; using this framework to define buffered gossip algorithms and local observation algorithms as generalizations for existing solutions to the decentralized consensus problem; highlighting the similarities between consensus algorithms within control theory and opinion dynamics within computational sociology, and showing how these research areas can be successfully combined to create new and powerful algorithms; and providing an empirical comparison between multiple information propagation algorithms

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations