An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
In the applications of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime since there is additional data transmissions that must be supported by the board...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/98/1/24p%20NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS.pdf http://eprints.uthm.edu.my/98/2/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/98/3/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20WATERMARK.pdf http://eprints.uthm.edu.my/98/ |
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| Summary: | In the applications of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime since there is additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this thesis is to increase the battery life of the IoT sensor node. To do so, a number of proposals are presented. First, an updating data strategy denoted as an efficient data collection and dissemination (EDCD) is proposed. EDCD aims to save the energy consumption of the IoT sensor board with multiple sensors by means of reducing the number of transmission packets, if no significant change is reported by the payload sensing block; second is proposed a validity of the measuring sensor reading at node level (VSNL) algorithm. VSNL aims to avoid transmitting any incorrect data, which will help in saving the energy consumption; third, an adaptive threshold and new metric for multivariate data reduction models such as principal component analysis – based (PCA-B) and multiple linear regression – based (MLR-B) have been proposed. In addition, proposed a payload data reduction algorithm (APRS). APRS aims to reduce the transmitted packet size for each sensed payload, which that will help in saving the energy of the IoT sensor board. This work provides an extensive analysis for the design and performance evaluation of real-time data collection model for multivariate sensors in IoT applications. Finally, an efficient real-time data collection model for multivariate sensors in IoT applications (RDCM). RDCM integrated EDCD, VSNL, PCA-B/MLR-B and APRS and the ability to prolong sensor board battery lifetime, which that satisfied by reducing number of transmissions and payload packet size, and also increase the accuracy of data validation. Performance of the proposed algorithms was evaluated through simulation by utilising various real-time datasets. The average of the total percentage of energy saved by applied RDCM to real-time data sets injected with various percentage of errors for all nodes is 98%. |
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