Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
Source abstract
Abstract
Network topology construction plays an important role in the application of large-scale unmanned aerial vehicle (UAV) swarm. Current researches usually perform the topology construction in terms of criteria of nodes energy consumption, transmission delay and network throughput, etc. However, another important criterion, the stability of swarm network topology, which is much critical for dynamic scenarios, has not been fully considered. In this paper, a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a novel Group Trend Similarity based double-head Clustering method(GTSC) is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account. The proposed method is effective when used to perform the network topology construction for UAV swarm, which is verified by the simulation results. Furthermore, in comparison with representative algorithms, the proposed GTSC method exhibits better performance on topology duration, network throughput, end-to-end delay and energy consumption balance especially in a large-scale swarm scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: UAV Applications and Optimization · Distributed Control Multi-Agent Systems · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu 611731, China
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