Modelling Virtual Sensors for Indoor Environments with Machine Learning
Authors: Polanski, D.M. and Angelopoulos, C.M.
Journal: Proceedings - 18th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2022
Pages: 222-228
ISBN: 9781665495127
DOI: 10.1109/DCOSS54816.2022.00046
Abstract:Virtual Sensors model the sensing operation of physical sensors deployed in an area of interest by generating sensory data with accuracy and precision close to those collected by physical sensors. Their use in applications such as augmenting the infrastructure of IoT facilities and test beds, monitoring and calibrating the operation of physical sensors, and developing Digital Twins of physical systems have led virtual sensors to attract research attention. Machine learning provides methods for modelling patterns in complex and big data generated by IoT sensing devices, allowing to model the behaviour of these devices. In this work, we investigate ML methods as means of implementation for virtual sensors. In particular, we evaluate the performance of six ML methods in terms of their effectiveness, accuracy and precision in generating sensory data based on data from physical sensors. In our study, we use a multi-modal dataset comprising IoT sensory data for temperature, humidity and illumination collected over a period of two years in an office space at University of Geneva. Our results show that the best performing model at predicting an output of a missing sensor is the Random Forest method, achieving MAPE error below 3%, 5% and 18% respectively for temperature, humidity and illuminance. The worst performing models were the linear radial basis function neural network and linear regression. In future research, we plan to deploy the best performing models natively on IoT devices, making use of tinyML and extreme edge computing methods.
https://eprints.bournemouth.ac.uk/37755/
Source: Scopus
Modelling Virtual Sensors for Indoor Environments with Machine Learning
Authors: Polanski, D.M. and Angelopoulos, C.M.
Journal: 18TH ANNUAL INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS 2022)
Pages: 222-228
ISSN: 2325-2936
DOI: 10.1109/DCOSS54816.2022.00046
https://eprints.bournemouth.ac.uk/37755/
Source: Web of Science (Lite)
Modelling Virtual Sensors for Indoor Environments with Machine Learning
Authors: Polanski, D.M. and Angelopoulos, C.M.
Conference: 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Pages: 222-228
ISBN: 9781665495134
ISSN: 2325-2936
Abstract:Virtual Sensors model the sensing operation of physical sensors deployed in an area of interest by generating sensory data with accuracy and precision close to those collected by physical sensors. Their use in applications such as augmenting the infrastructure of IoT facilities and test beds, monitoring and calibrating the operation of physical sensors, and developing Digital Twins of physical systems have led virtual sensors to attract research attention. Machine learning provides methods for modelling patterns in complex and big data generated by IoT sensing devices, allowing to model the behaviour of these devices. In this work, we investigate ML methods as means of implementation for virtual sensors. In particular, we evaluate the performance of six ML methods in terms of their effectiveness, accuracy and precision in generating sensory data based on data from physical sensors. In our study, we use a multi-modal dataset comprising IoT sensory data for temperature, humidity and illumination collected over a period of two years in an office space at University of Geneva. Our results show that the best performing model at predicting an output of a missing sensor is the Random Forest method, achieving MAPE error below 3%, 5% and 18% respectively for temperature, humidity and illuminance. The worst performing models were the linear radial basis function neural network and linear regression. In future research, we plan to deploy the best performing models natively on IoT devices, making use of tinyML and extreme edge computing methods.
https://eprints.bournemouth.ac.uk/37755/
Source: BURO EPrints