Advanced Signal Processing Techniques for Real-Time Systems in Edge Computing

With the rapid expansion of edge computing, real-time systems are increasingly deployed in environments such as IoT, autonomous vehicles, and industrial applications, where efficient signal processing is crucial for performance and decision-making. However, real-time signal processing in edge computing environments faces significant challenges, including limited computational resources, data transmission constraints, and strict latency requirements. Objective: This paper aims to explore advanced signal processing techniques specifically tailored for real-time systems in edge computing. The goal is to propose novel algorithms and strategies to enhance processing efficiency, reduce latency, and optimize resource usage in these environments. Methods: We propose a combination of traditional signal processing methods and emerging techniques, including: Adaptive Filtering: Improving real-time performance in dynamic environments. Compressed Sensing: Leveraging sparsity to reduce data processing and transmission overhead. Deep Learning Models: Applying convolutional and recurrent neural networks for accurate signal classification and anomaly detection. Distributed Signal Processing: Offloading computation tasks to distributed edge devices to balance load and reduce latency. Results: Experimental results demonstrate that the proposed techniques outperform traditional methods in terms of signal processing speed, resource utilization, and accuracy. In particular, the integration of deep learning models significantly improves classification accuracy for real-time signal detection, while compressed sensing reduces bandwidth requirements without compromising signal quality. Conclusion: The proposed signal processing methods effectively address the unique challenges posed by real-time systems in edge computing environments. These techniques can be applied to a wide range of applications, such as smart cities, industrial IoT, and autonomous systems, providing a solid foundation for the development of efficient, low-latency real-time systems.