Data Sourcing Random Access using Semantic Queries for Massive IoT Scenarios

Anders E. Kalør; Petar Popovski; Kaibin Huang

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arXiv · 2504.00619

Data Sourcing Random Access using Semantic Queries for Massive IoT Scenarios

Anders E. Kalør·Petar Popovski·Kaibin Huang

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Abstract

Efficiently retrieving relevant data from massive Internet of Things (IoT) networks is essential for downstream tasks such as machine learning. This paper addresses this challenge by proposing a novel data sourcing protocol that combines semantic queries and random access. The key idea is that the destination node broadcasts a semantic query describing the desired information, and the sensors that have data matching the query then respond by transmitting their observations over a shared random access channel, for example to perform joint inference at the destination. However, this approach introduces a tradeoff between maximizing the retrieval of relevant data and minimizing data loss due to collisions on the shared channel. We analyze this tradeoff under a tractable Gaussian mixture model and optimize the semantic matching threshold to maximize the number of relevant retrieved observations. The protocol and the analysis are then extended to handle a more realistic neural network-based model for complex sensing. Under both models, experimental results in classification scenarios demonstrate that the proposed protocol is superior to traditional random access, and achieves a near-optimal balance between inference accuracy and the probability of missed detection, highlighting its effectiveness for semantic query-based data sourcing in massive IoT networks.

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BibTeX RIS

Anders E. Kalør, Petar Popovski, Kaibin Huang. 2025-09-11. Data Sourcing Random Access using Semantic Queries for Massive IoT Scenarios. https://arxiv.org/abs/2504.00619

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