Power Consumption in Wireless Sensor Network: A Machine Learning Approach
DOI: 10.23977/cpcs.2022.060105 | Downloads: 500 | Views: 1567
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 AuthorHamid 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.
KEYWORDSWireless Sensor Network (WSN), Cloud Computing, Virtual Machine (VM), Machine Learning (ML), Power Consumption
CITE THIS PAPER
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: http://dx.doi.org/10.23977/cpcs.2022.060105.
 Sheng, Z., Mahapatra, C., Zhu, C., & Leung, V. C. (2015). Recent advances in industrial wireless sensor networks toward efficient management in IoT. IEEE Access, 3, 622-637.
 Navarro, M., Li, Y., & Liang, Y. (2014, June). Energy profile for environmental monitoring wireless sensor networks. In 2014 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6).
 Patel, M., Aggarwal, A., & Chaubey, N. (2019). Detection of Wormhole Attack in Static Wireless Sensor Networks. Advances in Computer Communication and Computational Sciences (pp. 463-471). Springer, Singapore.
 Agrawal, A., & Kaushal, S. (2015, September). A study on integration of wireless sensor network and cloud computing: requirements, challenges and solutions. In Proceedings of the Sixth International Conference on Computer and Communication Technology 2015 (pp. 152-157).
 Zhang, Y., Su, H., Yang, M., Zheng, D., Ren, F., & Zhao, Q. (2018). Secure DE duplication Based on Rabin Fingerprinting over Wireless Sensing Data in Cloud Computing. Security and Communication Networks.
 Luo, M., Luo, Y., Wan, Y., & Wang, Z. (2018). Secure and efficient access control scheme for wireless sensor networks in the cross-domain context of the IoT. Security and Communication Networks.
 Albath, J.(2008). Energy efficient clustering and secure data aggregation in wireless sensor networks. MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY 2008.
 Kulkarni, S. S., & Bhoi, A. D. (2017). Security and timing analysis of hybrid algorithm for ZigBee in wireless sensor network. International Journal of Engineering Science, Volume 13506.
 Dash, S. K., Sahoo, J. P., Mohapatra, S., & Pati, S. P. (2012, January). Sensor-cloud: assimilation of wireless sensor network and the cloud. In International Conference on Computer Science and Information Technology (pp. 455-464). Berlin: Springer.
 Kumar, L. D., Grace, S. S., Krishnan, A., Manikandan, V. M., Chinraj, R., & Sumalatha, M. R. (2012, April). Data filtering in wireless sensor networks using neural networks for storage in cloud. In 2012 International Conference on Recent Trends in Information Technology (pp. 202-205). IEEE.
 Shah, S. H., Khan, F. K., Ali, W., & Khan, J. (2013, March). A new framework to integrate wireless sensor networks with cloud computing. In IEEE Aerospace Conference (pp. 1-6). IEEE.
 Savas, O., Jin, G., & Deng, J. (2013, May). Trust management in cloud-integrated wireless sensor networks. International Conference on Collaboration Technologies and Systems (CTS) (pp. 334-341). IEEE.
 Misra, S., Chatterjee, S., & Obaidat, M. S.,et al. (2014). On theoretical modeling of sensor cloud: A paradigm shift from wireless sensor network. IEEE Systems journal, 11(2), 1084-1093.
 Chatterjee, S., & Misra, S. (2015, June). Optimal composition of a virtual sensor for efficient virtualization within sensor-cloud. In 2015 IEEE International Conference on Communications (ICC) (pp. 448-453). IEEE.
 Wang, T., Li, Y., Wang, G., Cao, J., Bhuiyan, M. Z. A., & Jia, W. (2017). Sustainable and efficient data collection from WSNs to cloud. IEEE Transactions on Sustainable Computing.
 Barbosa, L. C. M., Gomes, G., & Junior, A. C. A. (2019). Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks. The International Journal of Advanced Manufacturing Technology, 105(5-6), 2543-2556.
 Bocco, M., Willington, E., & Arias, M.,et al. (2010). Comparison of regression and neural networks models to estimate solar radiation. Chilean Journal of Agricultural Research, 70(3), 428-435.