Remote Monitoring of Correlated Sources Over Random Access Channels in IoT Systems

Kurzfassung

In the present-day communication technology involving fast-evolving Internet of Things (IoT) systems, accurate and timely information delivery is paramount. This thesis investigates an interesting concept of information freshness along with accuracy in a system where the transmitting sources are correlated. We focus on two key parameters to carry out this study: Average Age of Incorrect Information (AoII) and Average Estimation Error. We design a Markov Model to depict the intricacies of real-world correlated sources within communication systems. This Markov Model aids us in investigating our key parameters (AoII and Average error) under a range of correlation coefficients. We further explore different transmission and estimation methods to understand the variations in the above-mentioned parameters to comment on the optimal method for a given system condition. We further develop a strong theoretical framework for the transmission and estimation methods tailored to the unique challenges posed by correlated sources. This caters to our goal to understand the evolution of the AoII and Average Estimation Error throughout the range of the correlation coefficients. A rigorous investigation using empirical methods is conducted through carefully designed simulations to depict the real-world applications as closely as possible. This reveals interesting insights into the behavior of AoII and Average Error in the presence of correlated sources accessing a wireless random access channel. The results not only advance our understanding of these metrics but also provide practical guidance for optimizing communication systems under real-world conditions