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RML: A Robust Multi-hop Localization algorithm for irregular networks
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AI
DOI：10.1016/j.comcom.2025.108365.png)
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Abstract
En 中文
Node localization is prerequisite for multi-hop network applications. Traditional localization algorithms often assume nodes are uniformly distributed within regular, obstacle-free networks. However, this assumption rarely aligns with real-world network conditions. To address this, we propose a Robust Multi-hop Localization (RML) algorithm designed for irregular networks. First, a similarity metric is applied to compute distances between node pairs. Next, topological information from anchor nodes is used to infer a hop count threshold, filtering inaccurate distance measurements. Finally, depending on collinearity issues, either trilateration or an improved Black-winged Kite optimization algorithm is employed to determine node locations. Simulation results show that RML surpasses existing algorithms in efficiency, accuracy, and stability across diverse irregular networks. Specifically, RML achieves at least a 59.40% improvement in localization accuracy.
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