In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autonomous vehicles. To the best of our knowledge, this specific comparative analysis has not been previously conducted. While existing research has explored graph-based ANN algorithms, it has often been limited to single-threaded implementations on standard commodity hardware. Our study leverages the full computational and storage capabilities of edge devices, incorporating additional metrics such as insertion and deletion latency of new vectors and power consumption. This comprehensive evaluation aims to provide valuable insights into the performance and suitability of these algorithms for edge-based real-time tracking systems enhanced by nearest-neighbor search algorithms.

翻译：本文对部署于边缘设备上的多种基于图的近似最近邻搜索算法进行了实验比较，这些算法面向智能城市基础设施和自动驾驶等实时最近邻搜索应用。据我们所知，此类具体的对比分析尚未有前人开展。现有研究虽已探索基于图的近似最近邻算法，但通常局限于标准商用硬件上的单线程实现。本研究充分利用边缘设备的完整计算与存储能力，并引入了新向量插入与删除延迟及功耗等额外评估指标。此项全面评估旨在为基于最近邻搜索算法增强的边缘实时跟踪系统，提供关于这些算法性能与适用性的重要见解。