A machine learning approach to classifying algae concentrations

Daniels, Ethan T.; McPheron, Benjamin D.

doi:10.1109/urtc.2017.8284201

Cited by 5 publications

(1 citation statement)

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“…Soil monitoring methods were reported to have been affected by greenhouse effects. Ocean and marine SEM systems have been implemented using sensors, WSN and IoT and these methods have mainly suffered with cost, coverage and installation issues [40][41][42]. Air pollution control and AQM [1,10,11,16,[43][44][45] have been suggested using a mobile sensor network, wireless sensors and IoT devices that operate on AI and machine learning.…”

Section: Related Research and Studymentioning

confidence: 99%

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

414

109

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Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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