Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

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On the Security and Data Integrity of Low-Cost Sensor Networks for Air Quality Monitoring

Sensors ◽

10.3390/s18124451 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4451 ◽

Cited By ~ 10

Author(s):

Lan Luo ◽

Yue Zhang ◽

Bryan Pearson ◽

Zhen Ling ◽

Haofei Yu ◽

...

Keyword(s):

Air Quality ◽

Low Cost ◽

Measurement Data ◽

Data Integrity ◽

Pollution Monitoring ◽

Quality Monitoring ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Air Pollution Monitoring ◽

The Public

The emerging connected, low-cost, and easy-to-use air quality monitoring systems have enabled a paradigm shift in the field of air pollution monitoring. These systems are increasingly being used by local government and non-profit organizations to inform the public, and to support decision making related to air quality. However, data integrity and system security are rarely considered during the design and deployment of such monitoring systems, and such ignorance leaves tremendous room for undesired and damaging cyber intrusions. The collected measurement data, if polluted, could misinform the public and mislead policy makers. In this paper, we demonstrate such issues by using a.com, a popular low-cost air quality monitoring system that provides an affordable and continuous air quality monitoring capability to broad communities. To protect the air quality monitoring network under this investigation, we denote the company of interest as a.com. Through a series of probing, we are able to identify multiple security vulnerabilities in the system, including unencrypted message communication, incompetent authentication mechanisms, and lack of data integrity verification. By exploiting these vulnerabilities, we have the ability of “impersonating” any victim sensor in the a.com system and polluting its data using fabricated data. To the best of our knowledge, this is the first security analysis of low-cost and connected air quality monitoring systems. Our results highlight the urgent need in improving the security and data integrity design in these systems.

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Towards a Smart Data Transmission Strategy for IoT Monitoring Systems: Application to Air Quality Monitoring

2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS) ◽

10.1109/ictaacs48474.2019.8988119 ◽

2019 ◽

Author(s):

Razika Lounas ◽

Dhai Eddine Salhi ◽

Hocine Mokrani ◽

Rachid Djerbi ◽

Mohamed T. Bennai

Keyword(s):

Air Quality ◽

Data Transmission ◽

Quality Monitoring ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Transmission Strategy ◽

Smart Data

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A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

Royal Society Open Science ◽

10.1098/rsos.180889 ◽

2018 ◽

Vol 5 (9) ◽

pp. 180889 ◽

Cited By ~ 2

Author(s):

Zhengqiu Zhu ◽

Bin Chen ◽

Sihang Qiu ◽

Rongxiao Wang ◽

Yiping Wang ◽

...

Keyword(s):

Air Quality ◽

Measurement Data ◽

Monitoring Network ◽

Quality Monitoring ◽

Data Driven ◽

Atmospheric Environment ◽

Illustrative Case ◽

Air Quality Monitoring ◽

Detection Capability ◽

Data Driven Approach

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

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Indoor Air Quality Monitoring Systems

International Journal of Knowledge-Based Organizations ◽

10.4018/ijkbo.2021070101 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1-14

Author(s):

Rasha AbdulWahhab ◽

Karan Jetly Jetly ◽

Shqran Shakir

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Ad Hoc ◽

Quality Monitoring ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Research Activity ◽

Pollution Levels ◽

Closed Area

Research activity in the field of monitoring indoor quality systems has increased dramatically in recent years. Monitoring closed areas can reduce health-related risks due to poor or contaminated air quality. In the current COVID pandemic, the population has observed that improving ventilation in the closed area can significantly reduce infection risk. However, the significance of air quality statistics makes highly accurate real-time monitoring systems vital. In this paper, several researchers' protocols and the methodologies for monitoring a good high indoor air quality system are presented. The majority of the reviewed works are aimed to reduce air pollution levels of the atmosphere. The vast majority of the identified works utilized IoT and WSN technology to fix the partial access to sensed data, high cost, and non-scalability of conventional air monitoring systems. Furthermore, ad-hoc approaches are predominantly used to help society change its attitude and impose corrective actions to improve air quality. This paper presents a short but comprehensive review of several researchers works with different approaches to ecological trend analysis capabilities, drawing on existing literature works. Overall, the findings highlight the need for developing systematic protocols for these systems and establishing smart air quality monitoring systems capable of measuring pollutant concentrations in the air.

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

Sensors ◽

10.3390/s18093021 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3021 ◽

Cited By ~ 26

Author(s):

Zeba Idrees ◽

Zhuo Zou ◽

Lirong Zheng

Keyword(s):

Air Quality ◽

Power Consumption ◽

Real Time ◽

Low Cost ◽

Edge Computing ◽

Quality Monitoring ◽

Quality Data ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Computing Device

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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