Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models
MetadataShow full item record
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.
Fang , L , Ye , J & Dobson , S A 2019 , Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models . in Proceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) . , 8780572 , IEEE Computer Society , pp. 33-42 , 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) , Umeå , Sweden , 16/06/19 . https://doi.org/10.1109/SASO.2019.00014conference
Proceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019)
© 2019, IEEE. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/SASO.2019.00014
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.