Age of Information in Multicast Networks with Multiple Update Streams

We consider the age of information in a multicast network where there is a single source node that sends time-sensitive updates to $n$ receiver nodes. Each status update is one of two kinds: type I or type II. To study the age of information experienced by the receiver nodes for both types of updates, we consider two cases: update streams are generated by the source node at-will and update streams arrive exogenously to the source node. We show that using an earliest $k_1$ and $k_2$ transmission scheme for type I and type II updates, respectively, the age of information of both update streams at the receiver nodes can be made a constant independent of $n$. In particular, the source node transmits each type I update packet to the earliest $k_1$ and each type II update packet to the earliest $k_2$ of $n$ receiver nodes. We determine the optimum $k_1$ and $k_2$ stopping thresholds for arbitrary shifted exponential link delays to individually and jointly minimize the average age of both update streams and characterize the pareto optimal curve for the two ages

AoI-optimal Joint Sampling and Updating for Wireless Powered Communication Systems

This paper characterizes the structure of the Age of Information (AoI)-optimal policy in wireless powered communication systems while accounting for the time and energy costs of generating status updates at the source nodes. In particular, for a single source-destination pair in which a radio frequency (RF)-powered source sends status updates about some physical process to a destination node, we minimize the long-term average AoI at the destination node. The problem is modeled as an average cost Markov Decision Process (MDP) in which, the generation times of status updates at the source, the transmissions of status updates from the source to the destination, and the wireless energy transfer (WET) are jointly optimized. After proving the monotonicity property of the value function associated with the MDP, we analytically demonstrate that the AoI-optimal policy has a threshold-based structure w.r.t. the state variables. Our numerical results verify the analytical findings and reveal the impact of state variables on the structure of the AoI-optimal policy. Our results also demonstrate the impact of system design parameters on the optimal achievable average AoI as well as the superiority of our proposed joint sampling and updating policy w.r.t. the generate-at-will policy