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

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalInternational Journal of Distributed Sensor Networks
Volume14
Issue number1
DOIs
Publication statusPublished - 2018 Jan 1

Keywords

  • Adaptive sensing
  • drone coordination
  • environment monitoring
  • naive Bayes classification
  • sensor network

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Networks and Communications

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