Abstract
Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings.
Highlights
Environmental monitoring is an important and highly active research area
The wireless sensor we have developed is equipped with power harvesting capabilities and incorporates the CCS811 which is capable of measuring the total volatile organic compounds (TVOC) concentration value and equivalent CO2 levels
Advertisement packets to transmit the measured temperature, humidity, light intensity, and air quality in this work. This analysis was used for developing self-adaptive strategies for optimizing the power consumption of the sensing device based on the measurement of available energy and on the harvesting conditions
Summary
Environmental monitoring is an important and highly active research area. The observation of current values and trends of environmental parameters, such as temperature or level of harmful gases in the air, provides data that can help the detection of hazardous events and the assessment and implementation of appropriate actions in the case of climate change, population growth, urban sprawl, invasive species, and habitat destruction [1]. Organization report which states that in 2012 one in eight deaths globally was caused by air pollution exposure [2,3]. Different countries specify their own indices, the overall value is computed based on the concentration of different air pollutants such as PM2.5 and PM10 particles, and SO2 , NO2 , CO, and O3 chemical compounds. Another phenomenon with severe consequences, which is influenced by the continuously increasing impact of human activities over the environment, with carbon dioxide emissions as a primary cause, is climate change [4,5]
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