---
格式版本: 2
标题: "A Recommendation System Approach for Interference-Robust Sensor Subset Selection"
原文链接: "https://arxiv.org/abs/2608.11143"
发布日期: "2026-08-11"
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来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
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去重键: "https://arxiv.org/abs/2608.11143"
---

## Computer Science > Machine Learning

**arXiv:2608.11143** (cs)

## Title:A Recommendation System Approach for Interference-Robust Sensor Subset Selection

Authors:[Kaan Buyukkalayci](https://arxiv.org/search/cs?searchtype=author&query=Buyukkalayci,+K), [Kyle Pak](https://arxiv.org/search/cs?searchtype=author&query=Pak,+K), [Merve Karakas](https://arxiv.org/search/cs?searchtype=author&query=Karakas,+M), [Christina Fragouli](https://arxiv.org/search/cs?searchtype=author&query=Fragouli,+C)

[View PDF](https://arxiv.org/pdf/2608.11143) [HTML (experimental)](https://arxiv.org/html/2608.11143v1)

> Abstract:This paper develops a method for sensor-subset selection for tracking. Prior work showed that low-cost acoustic Received Signal Strength Indicator (RSSI) measurements can be used to recommend subsets of sensor nodes whose expensive sensing modalities, such as cameras, can achieve high tracking accuracy. While efficient, RSSI-based approaches are challenged by acoustic interference. We propose a recommendation-system-inspired framework that instead leverages frequency-band acoustic features and a Two-Tower Multi-Layer Perceptron (MLP) architecture to efficiently score candidate sensor subsets. Experimental results on outdoor vehicle-tracking deployments show that the proposed method can improve accuracy by around 20\\% over the RSSI baseline while maintaining the low computational overhead required for real-time selective sensing.

| Subjects: | Machine Learning (cs.LG) |
| --- | --- |
| Cite as: | [arXiv:2608.11143](https://arxiv.org/abs/2608.11143) \[cs.LG\] |
|  | (or [arXiv:2608.11143v1](https://arxiv.org/abs/2608.11143v1) \[cs.LG\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11143](https://doi.org/10.48550/arXiv.2608.11143) Focus to learn more  arXiv-issued DOI via DataCite (pending registration) |

## Submission history

From: Kaan Buyukkalayci \[[view email](https://arxiv.org/show-email/b111234c/2608.11143)\]  
**\[v1\]** Tue, 11 Aug 2026 17:01:44 UTC (384 KB)

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