Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Jung, Young Jin; Lee, Yang Koo; Lee, Dong Gyu; Lee, Yongmi; Nittel, Silvia; Beard, Kate; Nam, Kwang Woo; Ryu, Keun Ho

doi:10.3390/s111211235

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…The authors of the work [13] implemented a geo-sensor network that monitors air pollution and takes measures to prevent pollution, warning the building administrator to close the windows when there is a potential contamination hazard from outside.…”

Section: Related Workmentioning

confidence: 99%

Monitoring Environmental Conditions in Airports with Wireless Sensor Networks

et al. 2018

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In recent decades, the environment has suffered the negative consequences of industrial development and overexploitation, and the effect of growing population. There are many areas and sectors where actions can be taken to minimize negative environmental impacts. Despite the current technological advances in society, there is a pending debt to nature, environment and care of air quality. This paper presents a system based on Wireless Sensor Networks, whose objective is to monitor environmental conditions in airport environments. Since the proposed system allows managing notifications and alerts when dangerous conditions are detected, it can also be considered a procedure to protect critical infrastructures in general. Due to the importance of security requirements in these facilities, the system includes several cryptographic measures to provide robust authentication and encryption of information.

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Section: Related Workmentioning

confidence: 99%

Monitoring Environmental Conditions in Airports with Wireless Sensor Networks

et al. 2018

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“…A very recent work on design of sensor data processing steps for an air pollution monitoring system has been presented in [ 16 ]. This research focuses on visualizing collected data and experts would assess environment change based on the visualized patterns.…”

Section: Related Workmentioning

confidence: 99%

An Environmental Monitoring System for Managing Spatiotemporal Sensor Data over Sensor Networks

Lee

Vu³

et al. 2012

Sensors

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In a wireless sensor network, sensors collect data about natural phenomena and transmit them to a server in real-time. Many studies have been conducted focusing on the processing of continuous queries in an approximate form. However, this approach is difficult to apply to environmental applications which require the correct data to be stored. In this paper, we propose a weather monitoring system for handling and storing the sensor data stream in real-time in order to support continuous spatial and/or temporal queries. In our system, we exploit two time-based insertion methods to store the sensor data stream and reduce the number of managed tuples, without losing any of the raw data which are useful for queries, by using the sensors' temporal attributes. In addition, we offer a method for reducing the cost of the join operations used in processing spatiotemporal queries by filtering out a list of irrelevant sensors from query range before making a join operation. In the results of the performance evaluation, the number of tuples obtained from the data stream is reduced by about 30% in comparison to a naïve approach, thereby decreasing the query execution time.

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“…Smart cities are urban environments where the use of IoT devices spans across many disciplines such as environmental monitoring, traffic management, and early-warning systems [1,2,3,4]. Sensors are the backbone of a smart city because they enable situational and spatial awareness [5,6,7] for real-time monitoring of dynamic geographic phenomena.…”

Section: Introductionmentioning

confidence: 99%

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Alvarez

Morales

Kraak

2019

Sensors

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Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

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Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Cited by 13 publications

References 26 publications

Monitoring Environmental Conditions in Airports with Wireless Sensor Networks

Monitoring Environmental Conditions in Airports with Wireless Sensor Networks

An Environmental Monitoring System for Managing Spatiotemporal Sensor Data over Sensor Networks

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

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