4.8 Article

Deep-Learning-Based Multivariate Time-Series Classification for Indoor/Outdoor Detection

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 23, Pages 24529-24540

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3190555

Keywords

Computational modeling; Internet of Things; Time series analysis; Biological system modeling; Wireless fidelity; Time measurement; Predictive models; Deep learning (DL); indoor-outdoor detection (IOD); seamless navigation; self-attention; time-series classification (TSC)

Funding

  1. European Commission [860239]
  2. Marie Curie Actions (MSCA) [860239] Funding Source: Marie Curie Actions (MSCA)

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The topic of indoor outdoor detection (IOD) is gaining popularity. Existing models have limitations in scalability and accuracy. This article proposes treating IOD as a multivariate time-series classification problem and explores the performance of deep learning models. A new DL model is introduced that outperforms existing models in accuracy and computational cost.
Recently, the topic of indoor outdoor detection (IOD) has seen its popularity increase, as IOD models can be leveraged to augment the performance of numerous Internet of Things and other applications. IOD aims at distinguishing in an efficient manner whether a user resides in an indoor or an outdoor environment, by inspecting the cellular phone sensor recordings. Legacy IOD models attempt to determine a user's environment by comparing the sensor measurements to some threshold values. However, as we also observe in our experiments, such models exhibit limited scalability, and their accuracy can be poor. Machine learning (ML)-based IOD models aim at removing this limitation, by utilizing a large volume of measurements to train ML algorithms to classify a user's environment. Yet, in most of the existing research, the temporal dimension of the problem is disregarded. In this article, we propose treating IOD as a multivariate time-series classification (TSC) problem, and we explore the performance of various deep learning (DL) models. We demonstrate that a multivariate TSC approach can be used to monitor a user's environment, and predict changes in its state, with greater accuracy compared to conventional approaches that ignore the feature variation over time. Additionally, we introduce a new DL model for multivariate TSC, exploiting the concept of self-attention and atrous spatial pyramid pooling. The proposed DL multivariate TSC framework exploits only low power consumption sensors to infer a user's environment, and it outperforms state-of-the-art models, yielding a higher accuracy combined with a smaller computational cost.

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