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Research at St Andrews

Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wireless sensor networks empowered with low-cost sensing devices and wireless communications present an opportunity to enable continuous, fine-grained data collection over a wide environment. However, the quality of data collected is susceptible to the hardware conditions and also adversarial external factors such as high variance in temperature and humidity. Over time, the sensors report erroneous readings, which deviate from true readings. To tackle the problem, we propose an efficient self-monitoring, self-managing and self-adaptive sensing framework based on a dynamic hybrid Bayesian network that combines Hidden Markov Model and Dynamic Linear Model. The framework does not only enable automatic on-line inference of true readings robustly but also monitor the working status of sensor nodes at the same time, which can uncover important insights on hardware management. The whole process also benefits from the derived approximation algorithm and thus supports on-line one-pass computation with minimum human intervention, which make the accurate formal inference affordable for distributed edge processing.
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Details

Original languageEnglish
Title of host publicationProceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019)
PublisherIEEE Computer Society
Pages33-42
ISBN (Electronic)9781728127316
DOIs
Publication statusPublished - 16 Jun 2019
Event13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) - Umeå, Sweden
Duration: 16 Jun 201920 Jun 2019
Conference number: 13
https://saso2019.cs.umu.se/

Conference

Conference13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019)
Abbreviated titleSASO 2019
CountrySweden
CityUmeå
Period16/06/1920/06/19
Internet address

    Research areas

  • Self-management, Sensor networks, Machine learning, DLM, Markov switching model, State space model, Hybrid dynamic network

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