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

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4451 ◽  
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.

scholarly journals Design of Quadrotor UAV and Internet-of-Things Based Air Pollution Monitoring Systems

2020 ◽  
Vol 3 (4) ◽  
pp. 120
Author(s):  
Adnan Rafi Al Tahtawi ◽  
Erick Andika ◽  
Maulana Yusuf ◽  
Wildan Nurfauzan Harjanto
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Internet Of Things ◽  
Measurement Data ◽  
Data Retrieval ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Sensor System ◽  
Air Quality Monitoring ◽  
Air Pollution Monitoring

Air pollution is one of problems causing global warming that is currently taking a place. Several air quality monitoring devices usually located at the city center are only limited to display data at one point. Therefore, a mobile device to monitor air quality is needed so as to enable the monitoring in several points. This paper aims to design an air quality monitoring system based on quadrotor Unmanned Aerial Vehicle (UAV) and Internet-of-Things (IoT) technology. The sensor system is designed to detect CO, CO2, air quality, and temperature variables. This sensor systems was then integrated with quadrotor in order to make the monitoring process can be carried out at various points. Quadrotor was designed using Ardupilot Mega (APM) 2.6 as the flight controler. Measurement data from system sensor was transmitted wirelessly using internet network via Wi-Fi module. Based on the test results, the sensor system was able to detect concentration of several test gas and was linear to the output voltage. Quadrotor orientation parameters at takeoff produced transient responses in less than 1 second. The air pollution sensor parameter data could also be displayed every 10 seconds on the ThingSpeak and ThingView interfaces, and could be mapped based on the data retrieval coordinates.

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

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3021 ◽  
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.

scholarly journals A Proposal for Architectural Framework Using Internet of Things with Fog Computing for an Air Quality Monitoring System

2021 ◽  
Vol 2 (2) ◽  
pp. 1-14
Author(s):  
Aarti Rani ◽  
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Fog Computing ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Air Pollution Monitoring ◽  
Architectural Framework

Air Monitoring becomes a systematic approach for sensitivity and finding out the circumstances of the atmosphere. The major concern of air quality monitoring is to measure the concentration of pollution and other important parameter related to the contamination and provides information in real-time to make decisions at right time to cure lives and save the environment. This paper proposes an Architectural Framework for the air quality monitoring system based on Internet-of-Things (IoT) and via Fog computing techniques with novel methods to obtain real-time and accurate measurements of conventional air quality monitoring. IoT-based real-time air pollution monitoring system is projected to at any location and stores the measured value of various pollutants over a web server with the Internet. It can facilitate the process and filter data near the end of the IoT nodes in a concurrent manner and improving the Latency issue with the quality of services.

scholarly journals Design and Development of Air Quality Monitoring System for Solapur City Using Smart Technologies: WSN and IoT

2021 ◽  
Author(s):  
Tabbsum Hanif Mujawar ◽  
P. Prabhkar ◽  
Vijendra Chaudhary ◽  
Lalasaheb Deshmukh
Keyword(s):  
Air Quality ◽  
Monitoring System ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Air Quality Monitoring ◽  
Air Pollution Monitoring ◽  
Suspended Particulate ◽  
Smart Technologies ◽  
And Control

Owing to enhancement in technology there is inclination in miniaturization of devices which demands to build up stumpy expensive sensor, least powered and hardy devices. Accordingly, Wireless Sensor Networks (WSN) has gained significance in diverse applications: Farming, household, industries and environmental monitoring. Wireless sensor network system worn to monitor and control the air quality of an environment is developed. The air pollution monitoring system that measures temperature, humidity, SPM (Suspended Particulate Matter), NOx and CO are proposed. The conventional air quality monitoring system, prescribed by the Pollution Control Department, is tremendously pricey. Analytical measuring paraphernalia is lavish, time and power overriding, and can seldom be used for air quality exposure in real time. Endeavor has been completed to develop state of art monitoring system using commercially available standard pollutant gas sensors incorporated into a mote. An exact program made with LabVIEW is formed to constitute the measurements of sensing used in the established network. Remote monitoring of the system is made possible using IoT.

scholarly journals Key Concerns and Drivers of Low-Cost Air Quality Sensor Use

2022 ◽  
Vol 14 (1) ◽  
pp. 584
Author(s):  
Priyanka Nadia deSouza
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Data Visualization ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Quality Information ◽  
User Reviews ◽  
Air Pollution Monitoring ◽  
The Public

Low-cost sensors are revolutionizing air pollution monitoring by providing real-time, highly localized air quality information. The relatively low-cost nature of these devices has made them accessible to the broader public. Although there have been several fitness-of-purpose appraisals of the various sensors on the market, little is known about what drives sensor usage and how the public interpret the data from their sensors. This article attempts to answer these questions by analyzing the key themes discussed in the user reviews of low-cost sensors on Amazon. The themes and use cases identified have the potential to spur interventions to support communities of sensor users and inform the development of actionable data-visualization strategies with the measurements from such instruments, as well as drive appropriate ‘fitness-of-purpose’ appraisals of such devices.

scholarly journals A Nairobi experiment in using low cost air quality monitors

2017 ◽  
Vol 27 (2) ◽  
Author(s):  
P deSouza ◽  
V Nthusi ◽  
JK Klopp ◽  
BE Shaw ◽  
WO Ho ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Raising Awareness ◽  
Optical Particle Counter ◽  
Data Gap ◽  
Pm2.5 And Pm10 ◽  
Nations Environment

Many African cities have growing air quality problems, but few have air quality monitoring systems in place. Low cost air quality sensors have the potential to bridge this data gap. This study describes the experimental deployment of six low cost air quality monitors consisting of an optical particle counter Alphasense OPC-N2 for measuring PM1, PM2.5 and PM10, and Alphasense A-series electrochemical (amperometric) gas sensors: NO2-A43F, SO2-A4, NO-A4 for measuring NO2, NO and SO2 in four schools, the United Nations Environment Program (UNEP) headquarters and a community center in Nairobi. The monitors were deployed on May 1 2016 and are still logging data. This paper analyses the data from May 1 2016 to Jan 11 2017. By examining the data produced by these sensors, we illustrate the strengths, as well as the technical limitations of using low cost sensors for monitoring air quality. We show that despite technical limitations, sensors can provide indicative measurements of air quality that are valuable to local communities. It was also found that such a sensor network can play an important role in engaging citizens by raising awareness about the importance of addressing poor air quality. We conclude that these sensors are clearly a potentially important complement but not a substitute for high quality and reliable air quality monitoring systems as problems of calibration, certification, quality control and reporting remain to be solved.

scholarly journals IoT-based air quality monitoring systems for smart cities: A systematic mapping study

2021 ◽  
Vol 11 (4) ◽  
pp. 3470
Author(s):  
Danny Munera ◽  
Diana P. Tobon V. ◽  
Johnny Aguirre ◽  
Natalia Gaviria Gomez
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Systematic Mapping Study ◽  
Air Quality Monitoring ◽  
Mapping Study ◽  
Systematic Mapping

<p>The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context.</p>

scholarly journals Particulate Monitoring and Evaluation of the Low-cost Sensors Performance at the Middle East

2020 ◽  
Author(s):  
Yasser Alshehri ◽  
Mansour Alghamdi ◽  
Mamdouh Khoder ◽  
Fahd Almehmadi ◽  
Amer Bamuneef ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Air Quality ◽  
Middle East ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
World Health ◽  
Air Quality Monitoring ◽  
Spatial Coverage ◽  
Monitoring Stations

&lt;p&gt;&lt;strong&gt;Key Words:&lt;/strong&gt;&lt;br&gt;Air Quality; Air Pollution Monitoring; Low-Cost Sensors; Reference Methods, Microsensors, Experimental Campaign&lt;br&gt;&amp;#160;&lt;br&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Air quality in the Middle East (ME) is strongly affected by desert dust besides anthropogenic pollutants. The health hazards associated with particulate matter (PM) are the most severe in this desert region. The enhancement of Air quality monitoring is needed to implement abatement strategies and stimulate environmental awareness among citizens. Several techniques are used to monitor PM concentration. The air quality monitoring stations (AQMS) equipped with certified instrumentation is the most reliable option. However, AQMSs are quite expensive and require regular maintenance. Another option is low-cost sensors (LCS) that seen as&amp;#160;innovative&amp;#160;tools for future smart cities. They are cost-effective and allow to increase the spatial coverage of air-quality measurements as the number of conventional AQMS is generally quite small, so the current density of the monitoring stations in the Middle East is low.&lt;/p&gt;&lt;p&gt;&lt;br&gt;In this work, we evaluated the PM air-quality climatology in the major cities in Saudi Arabia (Jeddah, Riyadh, and Dammam) for four years between 2016 and continued until 2020. We used the measurement data that were conducted by&amp;#160;the Saudi Authority for Industrial Cities and Technology Zones (MODON)&amp;#160;using certified reference AQMS installed inside the suburban areas of the three major cities in Saudi Arabia. Also, we tested the performance of the five LCS systems for eight months, starting in May 2019 and continued until January 2020. For this purpose, we set AQMS with the PM reference instrumentation (based on beta-ray absorption) side-by-side with five different LCS systems (based on light scattering) in the industrial part of Jeddah city. We collected, filtered, validated PM data, and applied standard measurement and calibration procedures.&lt;br&gt;&lt;br&gt;The AQMS measurements show that in Summer, the daily mean PM concentrations exceed the World Health Organization (WHO) limits for PM2.5 and PM10 almost every day in Jeddah, Riyadh, and Dammam. The WHO limits are also frequently violated in the winter months. The AQMS measurements reliably show dust storm spikes when PM pollution is extremely high while all the LCSs fail to capture these severe events. We found that LCS and AQMS PM measurements are poorly correlated in Summer, but show slightly better results in fair-weather Winter days when humidity and temperature are low. But they still cannot capture severe dust events.&lt;/p&gt;

scholarly journals Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1697
Author(s):  
Alexey Penenko ◽  
Vladimir Penenko ◽  
Elena Tsvetova ◽  
Alexander Gochakov ◽  
Elza Pyanova ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Air Quality ◽  
Operator Equation ◽  
Source Identification ◽  
Measurement Data ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Spatial Coverage ◽  
Sensitivity Operator

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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