Linear Interval Approximation for Smart Sensors and IoT Devices
In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with min...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/949 |
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| author | Marin B. Marinov Nikolay Nikolov Slav Dimitrov Todor Todorov Yana Stoyanova Georgi T. Nikolov |
| author_facet | Marin B. Marinov Nikolay Nikolov Slav Dimitrov Todor Todorov Yana Stoyanova Georgi T. Nikolov |
| author_sort | Marin B. Marinov |
| collection | DOAJ |
| description | In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties. |
| first_indexed | 2024-03-09T23:08:37Z |
| format | Article |
| id | doaj.art-2d953693e0b440ea94e02fdda5ec4ac6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:08:37Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2d953693e0b440ea94e02fdda5ec4ac62023-11-23T17:48:01ZengMDPI AGSensors1424-82202022-01-0122394910.3390/s22030949Linear Interval Approximation for Smart Sensors and IoT DevicesMarin B. Marinov0Nikolay Nikolov1Slav Dimitrov2Todor Todorov3Yana Stoyanova4Georgi T. Nikolov5Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 1756 Sofia, BulgariaFaculty of Industrial Technology, Technical University of Sofia, 1756 Sofia, BulgariaFaculty of Industrial Technology, Technical University of Sofia, 1756 Sofia, BulgariaFaculty of Industrial Technology, Technical University of Sofia, 1756 Sofia, BulgariaFaculty of Industrial Technology, Technical University of Sofia, 1756 Sofia, BulgariaFaculty of Electronic Engineering and Technologies, Technical University of Sofia, 1756 Sofia, BulgariaIn this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties.https://www.mdpi.com/1424-8220/22/3/949approximationIoTlinearization techniquespiecewise approximationrecourse constrained devicessmart sensors |
| spellingShingle | Marin B. Marinov Nikolay Nikolov Slav Dimitrov Todor Todorov Yana Stoyanova Georgi T. Nikolov Linear Interval Approximation for Smart Sensors and IoT Devices Sensors approximation IoT linearization techniques piecewise approximation recourse constrained devices smart sensors |
| title | Linear Interval Approximation for Smart Sensors and IoT Devices |
| title_full | Linear Interval Approximation for Smart Sensors and IoT Devices |
| title_fullStr | Linear Interval Approximation for Smart Sensors and IoT Devices |
| title_full_unstemmed | Linear Interval Approximation for Smart Sensors and IoT Devices |
| title_short | Linear Interval Approximation for Smart Sensors and IoT Devices |
| title_sort | linear interval approximation for smart sensors and iot devices |
| topic | approximation IoT linearization techniques piecewise approximation recourse constrained devices smart sensors |
| url | https://www.mdpi.com/1424-8220/22/3/949 |
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