DETECTIF: Unified detection and correction of IoT faults in smart homes

• Main Authors: MALIICK, Madhumita, MISRA, Archan, GANGULY, Niloy, LEE, Youngki
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Transient Faults; United Fault Detection; Activities of Daily Living; IoT sensors; Smart home; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5956; https://ink.library.smu.edu.sg/context/sis_research/article/6959/viewcontent/Dectectif_2020_pv.pdf
• Institution: Singapore Management University

This paper tackles the problem of detecting a comprehensive set of sensor faults that can occur in IoT-instrumented smart homes customized to infer Activities of Daily Living (ADL) from the activation of sensor sets. Specifically, sensors can suffer faults that (a) span durations that vary between several seconds to hours, (b) can result in both missing or false-alarm sensor-events. Previous fault detection approaches are geared primarily to identify missing faults (absence of sensor readings) of a permanent (very long-lived) nature, or sporadic false-alarm events. We propose DetectIF, a fault-detection framework that detects faults of varying time duration, and identifies both missing and false-alarm sensor events. DetectIF's key novelties include developing rules capturing spatiotemporal correlations among sensors and augmenting those rules with statistical properties of such sensor-specific behavior. To test DetectIF under a variety of fault behavior, we develop a unified fault framework where the tuning of a couple of parameters allows us to generate and inject faults of desired type and duration into an underlying sensor stream. Experiments with such comprehensive fault data shows that DetectIF achieves 82-95% fault-detection accuracy, improving precision by a huge amount (33-66%) over competitive, state-of-the-art baselines. Moreover, we demonstrate the benefits of applying DetectIF on unmodified, benchmark smart home datasets: it is able to detect additional likely faults that prior fault detection approaches miss, and thus consequently achieve an average of 30% higher ADL recognition accuracy compared to prior state-of-the-art fault detection techniques.