A Recommendation System Approach for Interference-Robust Sensor Subset Selection
2026-08-11 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new way to choose which sensors to use when tracking objects like vehicles. Earlier methods used simple sound strength measurements (RSSI) to pick sensors but struggled with noisy sounds. Their approach uses detailed sound information from different frequency bands and a special neural network design called Two-Tower MLP to better score sensor groups. Tests showed their method improves tracking accuracy by about 20% compared to the old way, while still running fast enough for real-time use.
sensor-subset selectiontrackingReceived Signal Strength Indicator (RSSI)acoustic interferencefrequency-band featuresTwo-Tower Multi-Layer Perceptron (MLP)sensor networksreal-time sensingvehicle tracking
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.