Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study

Abstract

With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-temporal resolution. These systems use inexpensive and low-cost sensors, with lower accuracy as compared to robust systems. This fact has raised some concern regarding the quality of the data gathered by the IoT systems, which may compromise the performance of the environmental models. Considering the relevance of the data quality in this scenario, this paper presents a study of the data quality associated with IoT-based air quality monitoring systems. Following a systematic mapping method, and based on existing guidelines to assess data quality in these systems, we have identified the main Data Quality (DQ) dimensions and the corresponding DQ enhancement techniques. After analyzing more than 70 papers, we found that the most common DQ dimensions targeted by the different works are accuracy and precision, which are enhanced by the use of different calibration techniques. Based on our findings, we present a discussion on the challenges that must be addressed in order to improve data quality in IoT-based air quality monitoring systems.

Carbon based sensors for air quality monitoring networks; middle east perspective

“…Perception tier is the core of the system consisting of air quality and weather sensors, and devices to record the information and send it into network ( Gharaibeh et al, 2017 ). Network tier may consist of any communication protocol or network such cellular, LAN, WAN or wireless to efficiently disseminate the information retrieved from perception tier to user through application tier connecting various applications ( Sai et al, 2019 ; Bousiotis et al, 2021 ; Buelvas et al, 2023 ).…”

“…The role of the public is of the utmost importance to reach a sustainable clean environment reflected from their actions and awareness. Despite the importance of collaboration, several challenges hinder effective data sharing and cooperation in the Middle East which is linked to the unawareness or lack of understanding of the roles of stakeholders which usually leads to the limited accessibility to air quality data due to proprietary ownership, lack of standardized formats, and data restrictions by some stakeholders ( Buelvas et al, 2023 ; Srishti et al, 2023 ). Data recorded by diverse sources often faces issues of interoperability when it converges in a network.…”

“…IoT-based systems have brought significant advancements to air quality monitoring in the Middle East, leading to a deeper understanding of pollution trends, notable improvements, and positive changes in public awareness and policies. IoT-based systems provide real-time and precise data on air quality, allowing for the identification of pollution hotspots and the sources of contaminants ( Him et al, 2019 ; Sai et al, 2019 ; Bousiotis et al, 2021 ; Saini et al, 2021 ; Buelvas et al, 2023 ; Asghar et al, 2024 ). Continuous monitoring has revealed seasonal and diurnal pollution patterns, aiding in the development of targeted mitigation strategies ( Chen et al, 2015 ; Ammar et al, 2018 ; Din et al, 2019 ; Hassan et al, 2020 ; Saini et al, 2021 ; Saini et al, 2021 ; Buelvas et al, 2023 ).…”

“…IoT-based systems provide real-time and precise data on air quality, allowing for the identification of pollution hotspots and the sources of contaminants ( Him et al, 2019 ; Sai et al, 2019 ; Bousiotis et al, 2021 ; Saini et al, 2021 ; Buelvas et al, 2023 ; Asghar et al, 2024 ). Continuous monitoring has revealed seasonal and diurnal pollution patterns, aiding in the development of targeted mitigation strategies ( Chen et al, 2015 ; Ammar et al, 2018 ; Din et al, 2019 ; Hassan et al, 2020 ; Saini et al, 2021 ; Saini et al, 2021 ; Buelvas et al, 2023 ). The data-driven approach has enabled governments to measure the effectiveness of pollution control measures and adapt policies accordingly.…”

“…Use and development of low-cost air quality monitoring sensors should be encouraged at policy level to achieve high spatial and temporal coverage ( Margaritis et al, 2021 ; Bousiotis et al, 2023 ; Buelvas et al, 2023 ; Nobell et al, 2023 ). Similarly, the use of artificial intelligence-based algorithms should be encouraged to process the collected data.…”