Adaptive sensing scheme using naive Bayes classification for environment monitoring with drone

Yao Hua Ho*, Yu Te Huang, Hao Hua Chu, Ling Jyh Chen


研究成果: 雜誌貢獻期刊論文同行評審

9 引文 斯高帕斯(Scopus)


Environmental sensors are important for collecting data to understand environmental changes and analyze environmental issues. In order to effectively monitor environmental changes, high-density sensor deployment and evenly distributed spatial distance between sensors become the requirements and desired properties for such applications. In many applications, sensors are deployed in locations that are difficult and dangerous to reach (e.g. mountaintop or skyscraper roof). To collect data from those sensors, unmanned aerial vehicles are used to act as data mules to overcome the problem of collecting data in challenging environments. In this article, we extend the adaptive return-to-home sensing algorithm with a parameter-tuning algorithm that combines naive Bayes classification and binary search to adapt adaptive return-to-home sensing parameters effectively on the fly. The proposed approach is able to (1) optimize number of sensing attempts, (2) reduce oscillation of the distance for consecutive attempts, and (3) reserve enough power for drone to return-to-home. Our results show that the naive Bayes classification–enhanced adaptive return-to-home sensing scheme is able to avoid oscillation in sensing and guarantees return-to-home feature while behaving more cost-effective in parameter tuning than the other machine learning–based approaches.

期刊International Journal of Distributed Sensor Networks
出版狀態已發佈 - 2018 1月 1

ASJC Scopus subject areas

  • 一般工程
  • 電腦網路與通信


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