BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT
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| Format: | Preprint |
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2025
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| _version_ | 1866916976582983680 |
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| author | Abanto-Leon, Luis F. Salman, Muhammad Caceres-Najarro, Lismer Andres |
| author_facet | Abanto-Leon, Luis F. Salman, Muhammad Caceres-Najarro, Lismer Andres |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT Abanto-Leon, Luis F. Salman, Muhammad Caceres-Najarro, Lismer Andres Signal Processing 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. |
| title | BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.24588 |