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.
Highlights
Environmental information provided us important messages to understand environmental changes
We extend the adaptive returnto-home sensing (ARS) algorithm1 with a parametertuning algorithm that combines naive Bayes classification (NBC) and binary search (BS) to adapt ARS parameters effectively on the fly
The related works are discussed in section ‘‘Related works.’’ To make this article self-contented, the original ARS scheme and the extension of ARS with a parameter-tuning algorithm that combines NBC are introduced in section ‘‘ARS scheme.’’ Our experimental results are presented in section ‘‘Evaluation.’’ conclusions are drawn in section ‘‘Concluding remarks.’’
Summary
Environmental information provided us important messages to understand environmental changes. To overcome the problem of collecting data in challenging environments, UAVs can act as data mules to collect data from sensors Another type of sensing platform, called remote sensing, uses aircraft-based sensor technology to detect environmental changes and collect data without physically setting up fix sensors as the onsite observation approaches. The contributions of this article are as follows: (1) the extension of ARS scheme that combines NBC and BS for ARS parameters tuning is presented, (2) the proposed scheme is able to guarantee RTH by reserve enough of power while reducing oscillation between each sampling, and (3) our extensive simulation results show the ability of the extended ARS + NBC scheme in terms of reducing oscillation in sampling and dynamically adjusting energy reservation for RTH to address any changes (e.g. crosswind) in the environment which can cost addition power consumption. The related works are discussed in section ‘‘Related works.’’ To make this article self-contented, the original ARS scheme and the extension of ARS with a parameter-tuning algorithm that combines NBC are introduced in section ‘‘ARS scheme.’’ Our experimental results are presented in section ‘‘Evaluation.’’ conclusions are drawn in section ‘‘Concluding remarks.’’
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