Design and Evaluation of a Real-Time IoT Sensor Data Filtering System

Thesis Abstract

The exponential growth of Internet of Things (IoT) deployments has led to an unprecedented volume of sensor data generated in real time, presenting significant challenges in data management, transmission efficiency, and timely decision-making. This study aims to design and evaluate an effective real-time sensor data filtering system tailored to IoT environments, with the overarching goal of reducing data redundancy, optimizing network bandwidth, and enhancing the accuracy and responsiveness of data-driven applications. Specific objectives include developing a dynamic filtering algorithm that adapts to varying sensor conditions, implementing this algorithm within a prototype system, and empirically assessing its performance against existing Filtering Techniques through rigorous evaluation metrics. Employing a mixed-methods research design, the study combines qualitative analysis of existing filtering approaches with quantitative assessment of the proposed system. The population consists of sensor nodes within a smart agricultural setup comprising 150 heterogeneous sensors deployed across a 100-hectare farm, selected via stratified random sampling to capture diverse environmental parameters such as temperature, humidity, soil moisture, and light intensity. Data collection instruments include custom-developed sensor data logs, system performance logs, and surveys with system operators regarding system usability and responsiveness. For validation, the filtering system is implemented on a cloud-based IoT platform, and data collected over a three-month period are analyzed using statistical techniques—including regression analysis to evaluate filtering accuracy, ANOVA for system performance comparison, and time-series analysis to assess real-time responsiveness. The expected findings suggest that the proposed filtering system will significantly reduce data volume transmitted by approximately 40% without compromising data integrity, thereby improving network efficiency and lowering energy consumption. Additionally, the adaptive filtering algorithm is anticipated to deliver higher precision in relevant data extraction compared to conventional static filtering methods, with an average latency reduction of 25% in data delivery. The system's scalability and robustness are expected to be validated through simulation of increased sensor densities, confirming its applicability in large-scale IoT deployments. These findings are expected to fill existing gaps in IoT data management literature by providing a novel, adaptive, and resource-efficient filtering approach that balances data fidelity and transmission constraints. The study contributes to knowledge by advancing the theoretical understanding of adaptive filtering mechanisms in heterogeneous sensor networks and by offering practical insights into system design for resource-constrained environments. It also proposes a comprehensive framework for integrating dynamic filtering algorithms within existing IoT architectures, which can be adapted across various domains such as smart agriculture, health monitoring, and urban infrastructure management. Based on the findings, recommendations include the adoption of adaptive filtering algorithms for IoT networks to improve data quality and transmission efficiency, as well as suggestions for future research to explore machine learning integration for predictive filtering and automated system tuning. In conclusion, this research demonstrates that an intelligently designed real-time data filtering system can substantially improve IoT data management, offering a viable solution for addressing the burgeoning data influx in IoT ecosystems while maintaining high data integrity and system responsiveness.