Strategic Age of Information Under Different Correlation of Sources

We analyze a sensing system where multiple sources transmit status updates to a common receiver. We assume that the correlation of transmitted information allows updates from one source to enhance the information freshness of others. We study the objective of minimizing individual information staleness, quantified by the Age of Information (AoI), at the receiver's end. We evaluate both centralized and distributed optimization strategies. In the former case, we select the globally optimal transmission rates for each source to minimize the total average AoI of the system. For distributed optimization, sources are seen as players in a non-cooperative game of complete information, for which we compute the Nash equilibria. As an example, we consider a fixed correlation budget shared among two sources and evaluate the transmission rates depending on the specific level of correlation. Our results show that, under a centralized approach, it is convenient that only the source with more influential content transmits, while the other source reduces its data injection rate. In contrast, independent transmission in a distributed setup leads to greater congestion and higher average AoI. However, as correlation increases, the performance of the distributed system approaches that of the centralized model, indicating that decentralized management becomes effective in highly correlated scenarios.