Air Quality Monitoring System Based on ISO/IEC/IEEE 21451 Standards

Phala, Kgoputjo Simon Elvis; Kumar, Anuj; Hancke, Gerhard P.

doi:10.1109/jsen.2016.2555935

Cited by 87 publications

(21 citation statements)

References 34 publications

(37 reference statements)

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“…Despite research carried out on current technologies for indoor environments to estimate the position and orientation of mobile devices, the high cost of deployment to achieve accuracy is still a major challenge. In recent times, with the Internet-of-things and mobile devices enabling sensing [ 11 , 12 ] for a variety of consumer, environmental and industrial applications [ 13 , 14 , 15 , 16 , 17 ], sensors and embedded intelligence have become cheaper and easier to integrate into systems [ 15 ]. The main contribution of this work is the use of SURF and RANSAC algorithms to acquire data from vision and integrate it with inertial sensors to estimate the position and orientation of the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

167

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Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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Section: Introductionmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

167

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“…Air quality observing frameworks that can screen gas, for example, CO and Sox on surrounding air in a constant and can be gotten to with web line have been created [1]. Sensor component on this framework depends on Nanostructured zinc oxide meager film combined utilizing the wet substance course.…”

Section: Literature Surveymentioning

confidence: 99%

“…The fundamental target of IoT Pollution Monitoring System is that the Air contamination is a rising issue nowadays. It is obligatory to screen air quality [1], [2] and monitor it for a more beneficial future and solid living for all. Web of things (IoT) is picking up prominence step by step as it can change life making it simpler for people.…”

Section: Introductionmentioning

confidence: 99%

Advanced Air Pollution Detection using IOT and Raspberry PI

Al-Zaheiree*,

AL-Zubaidi,

Gaikwad

et al. 2020

IJRTE

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Air pollution becomes more complicated nowadays in many applications. In India, many cities are affecting air pollution. This is the increasing problem day by day. Various measures are taken to calculate the air quality and more preventative measures to be taken to control air pollution. Many air quality measures are taken to overcome the pollution but till now no improvement in the air quality is observed. In this paper, an enhanced air quality detection system is introduced to find the air polluted gases present in the environment. Air quality index was sensed gases like MQ2, MQ135, MQ7, etc. and displayed each and every calculation in the LCD display. New integrated technology is introduced with the integration of IoT to overcome the pollution problem.

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“…These capabilities are key for applications such as factory and industrial automation, distributed control systems, automotive systems and process monitoring [3,4]. CR is suitable for challenging environments such as underground monitoring [5], tele-intensive care units [6] or ambient environment monitoring [7].…”

Section: Introductionmentioning

confidence: 99%

Fast Convergence Cooperative Dynamic Spectrum Access for Cognitive Radio Networks

Chiwewe

Hancke

2018

IEEE Trans. Ind. Inf.

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Cognitive radio and dynamic spectrum access can reform the way that radiofrequency spectrum is accessed. Problems of spectrum scarcity, coexistence and unreliable wireless communication that affect industrial wireless networks can be addressed. In this paper, a game theoretic dynamic spectrum access algorithm that improves upon on a hedonic coalition formation algorithm for spectrum sensing and access is presented. The modified algorithm is tailored for faster convergence and scalability and makes use of a novel simultaneous multi-channel sensing and access technique. Results to demonstrate the performance improvements of the adapted algorithm are presented and the use of different decision rules are investigated revealing that a conservative decision rule for exploiting spectrum opportunities performs better than an aggressive decision rule in most scenarios. The algorithm that was developed could be a key enabler for future cognitive radio networks.

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Air Quality Monitoring System Based on ISO/IEC/IEEE 21451 Standards

Cited by 87 publications

References 34 publications

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Advanced Air Pollution Detection using IOT and Raspberry PI

Fast Convergence Cooperative Dynamic Spectrum Access for Cognitive Radio Networks

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