Arms: A Fine-Grained 3D AQI Realtime Monitoring System by UAV

Yang, Yuzhe; Zheng, Zijie; Bian, Kaigui; Jiang, Yun; Song, Lingyang; Han, Zhu

doi:10.1109/glocom.2017.8253968

Cited by 28 publications

(15 citation statements)

References 11 publications

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“…Then a simple spacial fitting is performed to create a more detailed distribution map of the air quality. To select sensing positions, the adopted indicator is called as Partial Derivative Threshold (PDT) [15]. V. POWER CONTROL For the sensing system, there is a intrinsic tradeoff between the power consumption and the precision.…”

Section: B Aerial Sensing: Detecting Location Selection and Routingmentioning

confidence: 99%

“…Due to the limited ventilation capacity of the sensing device, the UAV has to stay for a while at each position to make sure that the collected data is indeed based on the air at this position instead of the residual air from the previous location. A long hovering 15 time can guarantee the accuracy of data collecting but also consume additional power of the UAV, which in turn reduces the number of positions that the UAV can sense. Fig.…”

Section: B Aerial Sensing: Flight Planmentioning

confidence: 99%

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UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

Bai

Yang

et al. 2019

IEEE Network

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As air pollution is becoming the largest environmental health risk, the monitoring of air quality has drawn much attention in both theoretical studies and practical implementations. In this article, we present a real-time, fine-grained and power-efficient air quality monitoring system based on aerial and ground sensing. The architecture of this system consists of four layers: the sensing layer to collect data, the transmission layer to enable bidirectional communications, the processing layer to analyze and process the data, and the presentation layer to provide graphic interface for users. Three major techniques are investigated in our implementation, given by the data processing, the deployment strategy and the power control. For data processing, spacial fitting and short-term prediction are performed to eliminate the influences of the incomplete measurement and the latency of data uploading. The deployment strategies of ground sensing and aerial sensing are investigated to improve the quality of the collected data. The power control is further considered to balance between power consumption and data accuracy. Our implementation has been deployed in Peking University and Xidian University since February 2018, and has collected about 100 thousand effective data samples by June 2018.

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Section: B Aerial Sensing: Detecting Location Selection and Routingmentioning

confidence: 99%

Section: B Aerial Sensing: Flight Planmentioning

confidence: 99%

UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

Bai

Yang

et al. 2019

IEEE Network

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“…Reference [ 8 ] aims to detect PM only and notify the user via email using a low cost dust sensor and Raspberry Pi for data transmission. Reference [ 20 ] put their efforts into developing a system to measure the 3D AQI map. The researchers prototype a quad copter for 3D data collection and compare the results with official data and claim to achieve reasonable accuracy.…”

Section: Background and Related Workmentioning

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

103

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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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“…Therefore, it is preferred that large number of low-cost tiny sensing devices are deployed to provide air quality sensing for the regions with complicated terrain [8], [9]. Since the deployment of tiny sensing devices can be dense and the data collection can be frequent, the air quality distribution can be updated with low latency and high resolution [10], [11]. Such a solution creates a promising application of Internet-of-Things (IoT) in smart city [12], where massive data can be collected and analyzed [13].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Bai

Bian

et al. 2019

IEEE Internet Things J.

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Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation and optimization of our own air quality sensing system, which provides realtime and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

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Arms: A Fine-Grained 3D AQI Realtime Monitoring System by UAV

Cited by 28 publications

References 11 publications

UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

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

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

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