A Recommendation System Approach for Interference-Robust Sensor Subset Selection

Kaan Buyukkalayci; Kyle Pak; Merve Karakas; Christina Fragouli

A Recommendation System Approach for Interference-Robust Sensor Subset Selection

Machine Learning 2026-08-11 v1

Authors: Kaan Buyukkalayci , Kyle Pak , Merve Karakas , Christina Fragouli

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.

Keywords

sensor subset selection recommendation system acoustic interference Two-Tower MLP vehicle tracking selective sensing

Cite

@article{arxiv.2608.11143,
  title  = {A Recommendation System Approach for Interference-Robust Sensor Subset Selection},
  author = {Kaan Buyukkalayci and Kyle Pak and Merve Karakas and Christina Fragouli},
  journal= {arXiv preprint arXiv:2608.11143},
  year   = {2026}
}