{"ID":2863496,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24588","arxiv_id":"2509.24588","title":"BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.24588","url_pdf":"https://arxiv.org/pdf/2509.24588v1","authors":"[\"Luis F. Abanto-Leon\",\"Muhammad Salman\",\"Lismer Andres Caceres-Najarro\"]","published":"2025-09-29T10:51:36Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}