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Power Consumption in Wireless Sensor Network: A Machine Learning Approach

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DOI: 10.23977/cpcs.2022.060105 | Downloads: 508 | Views: 2191


Hamid Ali Abed AL-Asadi 1,2, Reham Hasan 3, Mohammad Nassr 3, Mohammad Anbar 3


1 Communications Engineering Department, Iraq University College, Basrah, 61004, Iraq
2 Computer Science Department, University of Basrah, Basrah, 61004, Iraq
3 Communication Technology Engineering Department, Tartous University, Syria

Corresponding Author

Hamid Ali Abed AL-Asadi


Power consumption in wireless sensor network is a serious issue as the location of the deployed sensors may prohibit a feasible power charging. This research work applies a machine learning technique in conjunction with cloud platform for enhancing the network life time of a wireless sensor network and making end-user experience more plausible. Raspberry Pi 3 model B has been used to create a private cloud in the proposed experiment and Arduino UNO to program the used wireless sensor network. Three machine learning techniques such as Time Series Prediction, Linear Regression and Artificial Neural Networks have been applied in the proposed work. Python with its different libraries and packages have been used in order to analyze the data on cloud resources. Dht22 sensors, Bluetooth & Wi-Fi shields have been used in the wireless sensor network. Results are very encouraging and suggests for its possible implementation in future wireless sensor network.


Wireless Sensor Network (WSN), Cloud Computing, Virtual Machine (VM), Machine Learning (ML), Power Consumption


Hamid Ali Abed AL-Asadi, Reham Hasan, Mohammad Nassr, and Mohammad Anbar, Power Consumption in Wireless Sensor Network: A Machine Learning Approach. Computing, Performance and Communication Systems (2022) Vol. 6: 24-37. DOI:


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