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

arXiv: https://arxiv.org/abs/2608.11143 · HTML: https://mlsanity.com/paper/2608.11143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [Artificial Intelligence of Things: A Survey](https://mlsanity.com/paper/2410.19998/) (2024)
- [The Internet of Battle Things](https://mlsanity.com/paper/1712.08980/) (2017)
- [A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks](https://mlsanity.com/paper/2308.08379/) (2023)

## Abstract (arXiv)

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.