IOT SYSTEM MODELING AND SIGNAL PROCESSING ON END DEVICES

Abstract

The rapid development of Internet of Things (IoT) systems increases the demand for efficient digital signal processing performed directly on end devices. Limited computational resources, strict energy constraints, and low-latency requirements make the development of optimized signal processing methods a critical research challenge. In such systems, the problem of detecting and processing broadband signals under noisy conditions becomes particularly important. The aim of this study is to develop and analyze digital correlation processing and matched filtering methods suitable for implementation on resource-constrained IoT devices. The objectives of the research include analyzing existing signal detection and matched filtering techniques, developing algorithms with reduced computational complexity, and evaluating their performance through signal modeling and simulation. As a result of the research, an adapted digital correlation processing method based on normalization and computational optimization is proposed. Signal processing simulations were performed for various signal shapes, including rectangular and broadband signals, in the presence of additive noise. The results demonstrate that the proposed method provides reliable signal detection while significantly reducing computational load and processing delay compared to classical correlation-based approaches. In conclusion, the developed methods can be effectively applied in IoT systems, sensor networks, and radio engineering applications, ensuring a balance between detection accuracy and computational efficiency. The obtained results contribute to the advancement of digital signal processing techniques for end devices and form a foundation for further research in energy-efficient data processing algorithms.