Consensus-based clustering and data aggregation in decentralized network of multi-agent systems

• Published in: PeerJ. Computer science 2023-08, Vol.9, p.e1445-e1445, Article e1445
• Main Authors: Damanik, Joshua Julian, Lim, Ming Chong, Jeong, Hyeon-Mun, Kim, Ho-Yeon, Choi, Han-Lim
• Format: Article
• Language: English
• Subjects: Agents and Multi-Agent Systems; Aggregation; Algorithms; Algorithms and Analysis of Algorithms; Clustering; Consensus; Distributed and Parallel Computing; Mathematical optimization; Multi-agent systems; Optimization; Optimization Theory and Computation; Robotics; Situational awareness
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1445

Multi-agent systems are promising for applications in various fields. However, they require optimization algorithms that can handle large number of agents and heterogeneously connected networks in clustered environments. Planning algorithms performed in the decentralized communication model and clustered environment require precise knowledge about cluster information by compensating noise from other clusters. This article proposes a decentralized data aggregation algorithm using consensus method to perform COUNT and SUM aggregation in a clustered environment. The proposed algorithm introduces a trust value to perform accurate aggregation on cluster level. The correction parameter is used to adjust the accuracy of the solution and the computation time. The proposed algorithm is evaluated in simulations with large and sparse networks and small bandwidth. The results show that the proposed algorithm can achieve convergence on the aggregated data with reasonable accuracy and convergence time. In the future, the proposed tools will be useful for developing a robust decentralized task assignment algorithm in a heterogeneous multi-agent multi-task environment.