Volume no :
6 |
Issue no :
1
Article Type :
Scholarly Article
Author :
P.Meenalochini
Published Date :
May, 2025
Publisher :
Journal of Science Technology and Research (JSTAR)
Page No: 1 - 15
Abstract : Environmental pollution poses a significant threat to public health and ecosystem stability worldwide. Rapid urbanization, industrial activities, and vehicular emissions have escalated the levels of harmful pollutants in the air, water, and soil, necessitating innovative approaches for real-time monitoring and management. This research presents a comprehensive framework for smart environmental sensing that leverages the Internet of Things (IoT) and cloud-based predictive models to detect, analyze, and forecast outdoor pollution levels with high accuracy and efficiency. The core of the proposed system is a distributed network of IoT-enabled sensing devices strategically deployed across urban and industrial areas to continuously monitor key environmental parameters such as particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), temperature, humidity, and atmospheric pressure. These sensors are integrated with microcontrollers capable of real-time data acquisition and preprocessing, minimizing noise and ensuring data reliability. The IoT devices communicate through low-power wireless protocols like LoRaWAN and NB-IoT to efficiently transmit data to centralized cloud servers, addressing challenges related to power consumption, coverage, and scalability. On the cloud platform, the collected sensor data undergoes extensive processing, storage, and analysis. Advanced machine learning algorithms and predictive models, including ensemble methods and deep learning architectures, are employed to identify pollution patterns, detect anomalies, and predict future pollution trends based on historical and real-time inputs. These models incorporate spatiotemporal correlations and environmental factors to enhance predictive accuracy, enabling timely alerts for hazardous pollution levels and supporting proactive decision-making by municipal authorities and environmental agencies. The system architecture emphasizes modularity and interoperability, facilitating easy integration with existing smart city infrastructure and environmental databases. An intuitive dashboard presents real-time pollution maps, trend graphs, and predictive insights accessible via web and mobile applications, promoting community awareness and enabling citizens to make informed health and lifestyle choices. Furthermore, the platform supports API access for third-party developers to build additional analytical tools and services, fostering an ecosystem of environmental innovation. Extensive field testing was conducted across multiple urban zones with varying pollution profiles to validate the system’s effectiveness. The IoT sensors demonstrated high sensitivity and reliability in capturing pollutant concentrations, while the predictive models consistently outperformed traditional statistical methods in forecasting pollution spikes. Results indicate an average prediction accuracy improvement of 15-20% and a reduction in false alarms, which is critical for maintaining public trust and optimizing resource allocation. Beyond pollution detection, the research explores the potential of integrating auxiliary data sources such as meteorological forecasts, traffic flow information, and industrial activity logs to refine predictions and support comprehensive environmental management strategies. The system’s adaptability to incorporate emerging sensing technologies and data analytics methods ensures its relevance in addressing evolving environmental challenges. This study contributes to the growing body of knowledge on smart environmental sensing by demonstrating how IoT and cloud computing can be synergistically combined to deliver scalable, cost-effective, and actionable pollution monitoring solutions. The proposed framework not only aids regulatory compliance and urban planning but also empowers communities by enhancing transparency and responsiveness to environmental health risks. In conclusion, the deployment of IoT-enabled smart sensors coupled with cloud-based predictive analytics represents a transformative approach to outdoor pollution detection. This integration facilitates continuous, real-time environmental monitoring, early warning of pollution hazards, and informed policymaking. Future work will focus on expanding sensor networks to rural and remote areas, improving model robustness against sensor failures, and exploring the integration of edge computing to reduce latency and bandwidth consumption. The outcomes of this research hold significant promise for advancing sustainable urban development and protecting public health in the face of increasing environmental pressures.
Keyword: IoT, environmental sensing, pollution detection, air quality monitoring, cloud computing, predictive models, machine learning, smart cities, real-time monitoring, particulate matter, atmospheric pollution, data analytics, wireless sensor networks, spatiotemporal analysis, urban environment
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Smart Environmental Sensing: Outdoor

Smart Environmental Sensing: Outdoor Extensive field testing was conducted across multiple urban zones with varying pollution profiles to
validate the system’s effectiveness. The IoT sensors demonstrated high sensitivity and reliability in
capturing pollutant concentrations, while the predictive models consistently outperformed traditional
statistical methods in forecasting pollution spikes. Results indicate an average prediction accuracy
improvement of 15-20% and a reduction in false alarms, which is critical for maintaining public trust and
optimizing resource allocation .

Smart Environmental Sensing: Outdoor

Beyond pollution detection, the research explores the potential of integrating auxiliary data sources such
as meteorological forecasts, traffic flow information, and industrial activity logs to refine predictions and
support comprehensive environmental management strategies. The system’s adaptability to incorporate
emerging sensing technologies and data analytics methods ensures its relevance in addressing evolving
environmental challenges.
This study contributes to the growing body of knowledge on smart environmental sensing by
demonstrating how IoT and cloud computing can be synergistically combined to deliver scalable, cost-
effective, and actionable pollution monitoring solutions. The proposed framework not only aids regulatory
compliance and urban planning but also empowers communities by enhancing transparency and
responsiveness to environmental health risks.
In conclusion, the deployment of IoT-enabled smart sensors coupled with cloud-based predictive analytics
represents a transformative approach to outdoor pollution detection. This integration facilitates
continuous, real-time environmental monitoring, early warning of pollution hazards, and informed
policymaking. Future work will focus on expanding sensor networks to rural and remote areas, improving
model robustness against sensor failures, and exploring the integration of edge computing to reduce
latency and bandwidth consumption . The outcomes of this research hold significant promise for
advancing sustainable urban development and protecting public health in the face of increasing
environmental pressures. This research presents a comprehensive framework for smart environmental sensing focused on
outdoor pollution detection by integrating Internet of Things (IoT) technology with cloud-based
predictive models.

Smart Environmental Sensing: Outdoor

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