A Markovian Model for the Analysis of Age of Information in IoT Networks

Age of Information (AoI) is a critical metric in status update systems as these systems require the fresh updates. This letter investigates the uplink of an Internet-of-Thing (IoT) network where L nodes transmit their information packets to a base station. The effects of the arrival rate of packets at the nodes, the number of nodes in the system, and queue length of each node have been studied by devising a discrete time Markov chain (MC) model. This model helps in predicting the values of AoI and probability of packet drops in such systems. The notion of first-in first-out is used for queuing, which transmits the oldest packet first, resulting in decreasing the overall AoI of the system. The results show that AoI increases with the increase in queue length, number of nodes and arrival rate and we quantify the aforementioned metrics using the MC model. The results found using the MC model are also validated using extensive simulations.

Automated summary

This paper investigates the uplink of an Internet-of-Thing (IoT) network using a discrete time Markov chain (MC) model to study the effects of packet arrival rate, number of nodes, and queue length on Age of Information (AoI) and packet drop probability. The results indicate that AoI increases with the increase in queue length, number of nodes, and arrival rate.