Abstract
Recently, capability of the wireless sensor network is beyond general its purpose with supporting from machine learning technique. Some WSNs deploy in harsh environment where is difficult to recharge energy for sensor, additionally, the spurious event is able to occur that consume a lot of energy to transmit the report message and leading to out of energy soon in WSNs. Therefore energy awareness is the most important consideration aspect of WSNs. In this paper, one method is proposed to reduce energy consumption by recognizing false event detection in cluster head in WSNs and predict burned area of forest adopt regression model.
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Acknowledgments
This work is result of studies on the “Leaders in INdustry-university Cooperation” Project, which is supported by the Korean Ministry of Education, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5045953).
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Tien, M.L., Elbasani, E., Choi, J.S. (2020). An Efficient Method for Wide Area Event Detection and Prediction Using Regression Model. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_93
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DOI: https://doi.org/10.1007/978-981-13-9341-9_93
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