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arXiv · 2509.24588

BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT

Luis F. Abanto-Leon·Muhammad Salman·Lismer Andres Caceres-Najarro
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Abstract

Leveraging received signal strength (RSS) measurements for indoor localization is highly attractive due to their inherent availability in ubiquitous wireless protocols. However, prevailing RSS-based methods often depend on complex computational algorithms or specialized hardware, rendering them impractical for low-cost access points. To address these challenges, this paper introduces buffer-aided RMSProp (BARProp), a fast and memory-efficient localization algorithm specifically designed for RSS-based tasks. The key innovation of BARProp lies in a novel mechanism that dynamically adapts the decay factor by monitoring the energy variations of recent gradients stored in a buffer, thereby achieving both accelerated convergence and enhanced stability. Furthermore, BARProp requires less than 15% of the memory used by state-of-the-art methods. Extensive evaluations with real-world data demonstrate that BARProp not only achieves higher localization accuracy but also delivers at least a fourfold improvement in convergence speed compared to existing benchmarks.

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Luis F. Abanto-Leon, Muhammad Salman, Lismer Andres Caceres-Najarro. 2025-09-29. BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT. https://arxiv.org/abs/2509.24588

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