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Simultaneous Sporadic Sensor Anomaly Detection for Smart Homes

Published: 24 January 2023 Publication History

Abstract

Dissemination of sensors and advances in techniques (e.g., network) has led to the opportunity for smart home. However, sensor malfunctions and difficult-to-diagnose characteristics hinder robust sensor system operation. Sensor anomaly detection systems for smart home have been proposed, but they target only a few specific types of sensor anomalies of a single sensor. In this work, we propose a sensor anomaly detection method based on Deep Neural Network (DNN), which automatically extracts critical features to detect the anomalies, even for simultaneous sporadic anomalies with complex data patterns. We leverage Hypersphere Classification (HSC) [14], the state-of-the-art DNN-based supervised outlier exposure method. We evaluate our proposed method on a public smart home sensor dataset. Our results show that the performances of the baselines drop up to 54.4% while ours drops up to 1.1%.

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  • (2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024

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    cover image ACM Conferences
    SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
    November 2022
    1280 pages
    ISBN:9781450398862
    DOI:10.1145/3560905
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 January 2023

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    Author Tags

    1. anomaly detection
    2. deep learning
    3. smart home
    4. unsupervised outlier exposure

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    SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;
    Overall Acceptance Rate 174 of 867 submissions, 20%

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    • (2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024

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