Assessing Event Fabrication Methods for Missing Events in Complex Event-Driven IoT Systems: A Smart Farming Case Study

In event-driven IoT systems, real-time data is crucial for effective complex event processing (CEP) and automated decision-making. However, missing sensor events due to network disruptions, sensor failures, or inconsistencies can degrade system performance and analytical accuracy. This study explores event fabrication techniques to reconstruct missing data in an IoTenabled smart farming scenario. We evaluate two methods - Naive and Exponential Smoothing with Linear Trend (ESLT) - by analyzing their accuracy and precision under various nonideal conditions. Experimental results highlight the trade-offs between these approaches, demonstrating that the Naive method, representing memoryless interpolation techniques, is more reliable for short-term gaps and sparse data, while ESLT, as a trend-aware statistical method, performs better with sufficient historical data but struggles in temporally sparse contexts. This distinction serves as a key finding, illustrating fundamental differences in event fabrication strategies. The findings provide insights into optimizing event fabrication strategies for real-time IoT applications, ensuring the continuity in event-driven architectures. Future research will explore adaptive methodologies for dynamically selecting the most suitable fabrication technique based on real-time conditions proposed by this study.