Real-time room occupancy estimation with bayesian machine learning using a single PIR sensor and microcontroller
Leech, Charles, Raykov, Yordan P., Ozer, Emre and Merrett, Geoff V. (2017) Real-time room occupancy estimation with bayesian machine learning using a single PIR sensor and microcontroller At IEEE Sensors Applications Symposium (SAS) 2017, Glassboro, United States. 13 - 15 Mar 2017. 6 pp.
- Accepted Manuscript
Restricted to Repository staff only until 13 March 2017.
Available under License University of Southampton Accepted Manuscript Licence.
This paper presents the implementation and deployment of a compute/memory intensive non-parametric Bayesian machine learning algorithm on a microcontroller unit (MCU) to estimate room occupancy in a Smart Room using a single analogue PIR sensor. We envisage an IoT device consisting of a resource-constrained MCU, PIR sensor and a battery running the occupancy estimation algorithm and operating over days or months without recharging or replacing the battery. Both hardware-independent and hardware-dependent optimizations are performed to reduce memory footprint and yet provide acceptable real-time performance while consuming less energy. We show a significant reduction in the on-chip memory usage in the MCUs by the algorithm through optimisation of the machine learning models and of the static memory footprint and dynamic memory usage. We also show that a low-end MCU does not meet the real-time requirements of the application without causing high average power consumption. However, a moderately high-performance MCU with a higher clock frequency and hardware floating-point unit provides 19x improvement in the execution time of the algorithm, better meeting the real-time specification of the application and reducing power consumption. Further, we estimate the battery lifetime of the IoT device if it operates continuously in a Smart Room. With a typical size battery, an IoT device consisting of a Cortex-M4F MCU and PIR sensor can operate for more than a month without replacement or recharging of the battery while running the compute-intensive Bayesian machine learning algorithm.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works|
|Venue - Dates:||IEEE Sensors Applications Symposium (SAS) 2017, Glassboro, United States, 2017-03-13 - 2017-03-15
|Organisations:||Electronics & Computer Science|
|Date Deposited:||18 Feb 2017 00:22|
|Last Modified:||10 Mar 2017 10:48|
|Further Information:||Google Scholar|
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