Real-time room occupancy estimation with Bayesian machine learning using a single PIR sensor and microcontroller
Real-time room occupancy estimation with Bayesian machine learning using a single PIR sensor and microcontroller
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.
Leech, Charles
6ba70c54-3792-41cd-a8d6-9e8884ae004f
Raykov, Yordan P.
f812dd21-2a4b-4fe8-b551-90a9b521b44b
Ozer, Emre
07d738b5-0b1c-4291-8284-9179c191c3d2
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
March 2017
Leech, Charles
6ba70c54-3792-41cd-a8d6-9e8884ae004f
Raykov, Yordan P.
f812dd21-2a4b-4fe8-b551-90a9b521b44b
Ozer, Emre
07d738b5-0b1c-4291-8284-9179c191c3d2
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
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.
IEEE Sensors Applications Symposium (SAS) 2017, Rowan Univeristy, Glassboro, United States.
13 - 15 Mar 2017.
6 pp
.
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Conference or Workshop Item
(Paper)
Abstract
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.
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Accepted/In Press date: 28 January 2017
Published date: March 2017
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Venue - Dates:
IEEE Sensors Applications Symposium (SAS) 2017, Rowan Univeristy, Glassboro, United States, 2017-03-13 - 2017-03-15
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 405697
URI: http://eprints.soton.ac.uk/id/eprint/405697
PURE UUID: 1a9a7bd1-4319-4894-8b90-d9cb2d8dfab2
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Date deposited: 18 Feb 2017 00:22
Last modified: 14 Dec 2024 05:02
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Contributors
Author:
Charles Leech
Author:
Yordan P. Raykov
Author:
Emre Ozer
Author:
Geoff V. Merrett
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