Identifying the module structure of swarms using a new framework of network-based time series clustering. (May 2021)

• Record Type: Journal Article
• Authors: Gu, Kongjing; Mao, Ziyang; Duan, Xiaojun; Wu, Guanlin; Yan, Liang
• Is Part Of: Engineering applications of artificial intelligence. Volume 101(2021)
• Publication Date: 2021-05
• Subjects: Time series clustering -- Snapshot -- Network -- Identification -- UAV swarm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104214
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Swarm is a collective motion phenomenon whose dynamic mechanism and cooperation structure could be identified based on observations. Unmanned Aerial Vehicles (UAV) is a special artificial swarm with unique rules and structures. Therefore, corresponding identification methods need to be developed. One critical identification problem is distinguishing the swarm's cooperation structure, which is usually clustered and grouped to achieve stability of behaviors and low communication cost. This paper proposes a framework of Overlay Network Integrated Time series clustering (ONIT) to identify the UAV swarm structures based on trajectories. The framework consists of Snapshot, Net Growth and Net Split. It can fuse with most distance functions in time series clustering, achieving high accuracy, update ability, and fault tolerance with various datasets. We create point-based and sliding window-based snapshots, allowing the framework compatible with more methods. In particular, the Dynamic Time Wrapping (DTW) correspondence in point-based snapshots shows the high scalability of the framework, and the Euclidean Distance (ED) correspondence shows that the framework can still significantly improve the accuracy while maintaining the simplicity of calculation. The test results show that the fused ONIT-clustering algorithms, especially the point-based ones, outperform original time series clustering methods separately in simulation datasets of UAV swarms and UCR repository by 28% and 27%. In summary, the proposed framework is a flexible and scalable time series clustering method that can solve various time series clustering problems especially the trajectory clustering of the UAV swarm and has great potential for general time series analysis.