Polluino: An efficient cloud-based management of IoT devices for air quality monitoring

Fioccola, Giovanni B.; Sommese, Raffaele; Tufano, Imma; Canonico, Roberto; Ventre, Giorgio

doi:10.1109/rtsi.2016.7740617

Cited by 57 publications

(26 citation statements)

References 11 publications

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“…Sensor Module : This module contains low cost electrochemical sensors from the MQ series (MQ-135, MQ-6, MQ-7, MQ-9) for measuring indoor pollutants and hazardous gases [ 10 , 35 ]. The GP2Y1014AU0F, was installed to measure the dust & particle matters.…”

Section: Proposed Edge Computing Based Iot Architecture For the Aimentioning

confidence: 99%

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Idrees

Zou

Zheng

2018

Sensors

105

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With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

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Section: Proposed Edge Computing Based Iot Architecture For the Aimentioning

confidence: 99%

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Idrees

Zou

Zheng

2018

Sensors

105

View full text Add to dashboard Cite

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“…IoT-based air quality monitoring has received intensive research effort in recent years, e.g., [4,7,17,9,22]. These efforts focus on the implementation of the IoT, whilst rarely introduce how to manage IoT data.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Monitoring System and Benchmarking

Liu

Nielsen

2017

Big Data Analytics and Knowledge Discovery

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“…Fioccola et al [ 7 ] described a system based on Arduino board and a cloud-based platform that manages data obtained from AQ sensors. A comparison between two cloud computing service models and between two IoT communication protocols has been performed.…”

Section: Introductionmentioning

confidence: 99%

A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

Benammar

Abdaoui

Ahmad

et al. 2018

Sensors

180

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The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO2, CO, SO2, NO2, O3, Cl2, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.

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Polluino: An efficient cloud-based management of IoT devices for air quality monitoring

Cited by 57 publications

References 11 publications

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Air Quality Monitoring System and Benchmarking

A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

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