A Recommendation System Approach for Interference-Robust Sensor Subset Selection

2026-08-11

AI Summary

This study addresses the challenge of achieving high-precision target tracking in acoustically noisy environments, where conventional RSSI-based sensor selection methods suffer from significant performance degradation. To overcome this limitation, the work introduces, for the first time, a recommendation-system-inspired approach to interference-resilient sensor subset selection. It proposes an efficient scoring framework that leverages acoustic band features and a two-tower multilayer perceptron (Two-Tower MLP) to replace the interference-prone RSSI metric, enabling real-time evaluation and optimal selection of candidate sensor subsets. Experimental results on outdoor vehicle tracking demonstrate that the proposed method improves tracking accuracy by approximately 20% over RSSI-based baselines while maintaining low computational overhead, thereby satisfying real-time operational requirements.

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.

Problem

• sensor subset selection
• acoustic interference
• tracking
• recommendation system
• RSSI

Innovation

• recommendation system
• sensor subset selection
• acoustic interference robustness
• Two-Tower MLP
• frequency-band acoustic features