Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Abstract

Determining the network size is a critical process in numerous areas (e.g., computer science, logistic, epidemiology, social networking services, mathematical modeling, demography, etc.). However, many modern real-world systems are so extensive that measuring their size poses a serious challenge. Therefore, the algorithms for determining/estimating this parameter in an effective manner have been gaining popularity over the past decades. In the paper, we analyze five frequently applied distributed consensus gossip-based algorithms for network size estimation in multi-agent systems (namely, the Randomized gossip algorithm, the Geographic gossip algorithm, the Broadcast gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol). We examine the performance of the mentioned algorithms with bounded execution over random geometric graphs by applying two metrics: the number of sent messages required for consensus achievement and the estimation precision quantified as the median deviation from the real value of the network size. The experimental part consists of two scenarios—the consensus achievement is conditioned by either the values of the inner states or the network size estimates—and, in both scenarios, either the best-connected or the worst-connected agent is chosen as the leader. The goal of this paper is to identify whether all the examined algorithms are applicable to estimating the network size, which algorithm provides the best performance, how the leader selection can affect the performance of the algorithms, and how to most effectively configure the applied stopping criterion.

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs

“…In this paper, our focus was on addressing the leader-following consensus problem using a distributed dynamic event-triggered control protocol with nonlinear dynamics under directed graphs. To some extent, this paper can be seen as an extension of Reference [27,30]. Our results demonstrate that the proposed control strategy can achieve exponential synchronization and, importantly, ensures that Zeno behavior does not occur for any node.…”

“…Yin et al presents a comparative study of distributed consensus gossip algorithms for network size estimation. The results are helpful in selecting the optimal consensus gossip algorithm for network size estimation [27]. Yan et al introduces the adaptive memory-event-triggered control into T-S fuzzy systems [28].…”

Gossip-Based Monitoring Protocol for 6G Networks

“…Thus, gossip protocols are mainly used as consensus protocols between neighbors [11], [34], [35]. In addition to blockchain-specific proposals, the distributed nature of modern computing paradigms, such as in 6G, has stimulated a revival of gossip as (i) randomized consensus protocol in more general contexts promoting fundamental research [36]- [38], and (ii) technology to efficiently monitor systems on a large scale [39].…”

Dynamic Algorithm for Interference Mitigation Between Cells in Networks Operating in the 250 MHz Band

“…• reinforcement learning (RL). In this paper, we use RL as the approach for optimizing a specific cost function with specific constraints to optimize the system throughput [26], [27]. It is a trial-and-error process where an agent interacts with an unknown environment in a sequence of discrete time steps to achieve a specific target or task.…”