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Vol.1 , No. 1, Publication Date: Feb. 12, 2018, Page: 1-5
| [1] | Mohammad Abdulaziz Alwadi, Faculty of Information Sciences and Engineering, the University of Canberra, Canberra, Australia. |
| [2] | Girija Chetty, Faculty of Information Sciences and Engineering, the University of Canberra, Canberra, Australia. |
In this paper a proposed energy efficient sensor reduction method for Intel Berkeley lab wireless sensor network data set based on machine learning. The experimental work in this paper using publicly available WSN dataset to show the possibility to reduce the number of sensors used in order to enhance the energy efficiency where the system resources and energy are always a key issue in Wireless sensor networks. The main concept is to perform certain experiments on the Intel Berkeley to come up with improved energy reduction method associated with the least number of sensors used to get better sensor life time and performance. Rest of the paper is organized as follows. Next sections describing the background and introduction, the details of the dataset used, and section 3 describes the sensor selection and routing approach, where the details of experimental results obtained are presented, and the paper concludes in Section 4.
Keywords
Machine Learning, Wireless Sensor Networks, Dataset, Sensor Life Time
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