Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural Network
Abstract
Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is crucial in order to provide high-quality intelligent services. However, sensor data delivery can be interrupted for various reasons, such as sensor malfunction, network failures, and external attacks. Thus, only data from a partial set of sensors may be available. We call it the missing sensor problem. This problem can lead to severe performance degradation at inference time by neural-network-based recognition models trained on the complete sensor set. This paper enhances the robustness of neural network models to the missing sensor problem by introducing a novel feature reconstruction module, named the graph recovery module, that handles missing sensors directly inside the network. Specifically, we consider topology-aware IoT applications, where sensors are placed on a physically interconnected network. We design a novel neural message passing mechanism that logically mimics physical network topology, based on recent advances in graph neural networks (GNNs). We rely on a spatial locality assumption, where only correlations between physically connected sensors are explicitly explored. When encountering missing sensors, information is passed from available sensors to missing sensors to be used to reconstruct their features. Moreover, at each message passing step, we utilize a gating mechanism inspired by Gated Recurrent Units (GRUs) to automatically control information flow between available sensors and missing sensors. We empirically evaluate the reconstruction performance of the graph recovery module with two representative IoT applications; human activity recognition (HAR) and electroencephalogram (EEG)-based motor-imagery classification, on three public datasets. Two different backbone networks are utilized for the tasks. Our design is shown to effectively maintain model performance, suffering only 7% to 18% accuracy loss when as much as 90% of sensors are removed, compared to a drop of 15% to 47% in the accuracy of competing state-of-the-art algorithms under the same conditions. The accuracy gap is largest when more sensors are missing.
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The available devices in multi-device WHAR are an arbitrary subset of the total devices, which is unpredictable and would severely impact the recognition performance of existing methods.
“As a result, the available devices are an arbitrary subset of the total devices, which is unpredictable and would severely impact the recognition performance of existing methods.”
From Spatial-Temporal Masked Autoencoder for Multi-Device Wearable Human Activity Recognition (2023) · cites this paper as [42]
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- Authors
- Liu, S., Yao, S., Huang, Y., Liu, D., Shao, H., Zhao, Y., Li, J., Wang, T., Wang, R., Yang, C., & Abdelzaher, T.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Microsoft (United States) (US)
- Pinterest (United States) (US)
- ORCID
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2020-09-04
- Volume
- 4
- Issue
- 3
- Pages
- 1-31
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W3083256489
- Cited by
- 20
- References
- 46
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 6 · 2021: 2 · 2020: 2
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Funders
- DARPA (W911NF-17-C-0099)
- DTRA (HDTRA118-1-0026)
- Army Research Laboratory (W911NF-17-20196)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Internet of Things, graph neural networks, gated graph neural networks, feature reconstruction, message passing, spatial locality, human activity recognition, EEG motor imagery classification, missing data
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