Design of an IoT-based Sensor Network for Urban Air Quality Monitoring

Thesis Abstract

Rapid urbanization and industrialization have significantly contributed to deteriorating air quality in metropolitan areas, posing serious public health risks and environmental challenges. Despite the recognized need for efficient air quality monitoring systems, existing approaches often lack real-time data, spatial coverage, and adaptability, which hampers timely interventions and policy formulation. This study aims to design an Internet of Things (IoT)-based sensor network tailored specifically for urban air quality monitoring, with the objective of providing real-time, high-resolution data to stakeholders for better decision-making. The research evaluates the technological, operational, and socio-economic feasibility of deploying a low-cost, scalable IoT sensor network across urban environments, emphasizing sensor accuracy, data transmission reliability, and energy efficiency. The methodology adopts a mixed-methods research design, integrating quantitative experimental approaches with qualitative assessments. The targeted population includes air quality sensors, urban environmental data, and city inhabitants' perceptions. A sample of 150 low-cost air quality sensors, comprising particulate matter (PM2.5 and PM10), nitrogen dioxide, and ozone sensors, will be deployed across three urban districts representing different pollution profiles. Stratified random sampling will be employed to select sensor locations to ensure spatial representativeness. Data collection instruments encompass sensor calibration protocols, wireless communication modules, and survey questionnaires designed to gauge community awareness and acceptance. Sensor calibration will be done using standard reference monitors to ensure measurement validity, while data transmission reliability will be monitored through test-bed experiments over a six-month period. Analytical techniques will include descriptive statistics for initial data profiling, regression analysis to determine sensor accuracy and predictive performance, and ANOVA to compare spatial variability in air quality levels. Thematic analysis of qualitative data from community surveys will complement the quantitative findings regarding user perceptions and engagement. The anticipated results include establishing a robust, energy-efficient IoT sensor network capable of delivering accurate, real-time air quality data with spatial granularity at the neighborhood level. It is expected that regression analysis will demonstrate strong correlation coefficients (>0.85) between sensor and reference monitor readings, validating the network’s measurement fidelity. Spatial analysis via ANOVA is projected to reveal significant differences (p<0.05) in pollutant concentrations across the urban districts, highlighting pollution hotspots. Qualitative insights are anticipated to show high community acceptance and willingness to participate in urban air quality monitoring, provided that data privacy and usage concerns are addressed. These findings will contribute to the emerging body of knowledge on scalable IoT solutions for environmental monitoring, specifically advancing understanding of sensor deployment strategies, data management frameworks, and socio-technical integration in urban settings. The study’s contribution to knowledge lies in developing a comprehensive prototype of an IoT-enabled air quality monitoring system that integrates sensor technology, wireless networking, and data analytics within an urban environmental context. It offers practical insights for policymakers, urban planners, and technologists seeking sustainable, community-engaged solutions for air pollution management. Furthermore, the research underscores the importance of sensor accuracy validation and community participation models in deploying effective IoT environmental monitoring frameworks. In conclusion, this research demonstrates the viability of low-cost IoT sensor networks as a tool to enhance urban air quality surveillance. The study recommends the adoption of standardized calibration protocols, integration of cloud-based data management systems, and the promotion of community awareness programs to maximize the benefits of IoT-enabled environmental monitoring. Future studies should explore the integration of machine learning algorithms to improve predictive capabilities and investigate long-term maintenance strategies for sensor networks to ensure system sustainability. This research significantly contributes to advancing sustainable urban environmental health monitoring through innovative use of IoT technology.