Mathematical Modeling of Real-Time Signal Processing in IoE

Abstract

This research suggests a comprehensive mathematical modelling approach for the efficient processing of signals in real-time within Internet of Everything (IoE) environments. The model employs state-of-the-art methods in signal processing, control theory, and machine learning to address the challenges posed by the constantly changing and diverse IoE networks. The proposed architecture employs real-time data processing and collection to improve the overall resilience of the network, increase the accuracy of decision-making, and improve system performance. Adaptive filtering strategies were implemented in this investigation to maintain signals in dynamic network environments. Additionally, robust algorithms were developed and executed for anomaly identification, data compression, and noise reduction. The proposed model's potential to significantly enhance the capabilities of IoE systems is demonstrated through extensive simulations and real-world case studies, which serve to assess its utility.