Noise Monitoring for Enhanced Living Environments Based on Internet of Things

Marques, Gonçalo; Pitarma, Rui

doi:10.1007/978-3-030-16187-3_5

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…They provide easy access to the data obtained from different locations with mobile computing. These technology-inspired projects have been applied to the multidisciplinary research fields, such as agriculture [74][75][76][77][78], noise pollution supervision [79][80][81], indoor quality monitoring [82,83], and other numerous applications for Enhanced Living Environments and occupational health.…”

Section: Indoor Air Quality Monitoring: Architectures Algorithms and ...mentioning

confidence: 99%

Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities

et al. 2020

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Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

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