ﻻ يوجد ملخص باللغة العربية
We study age of information in a status updating system that consists of a single sampler, i.e., source node, that sends time-sensitive status updates to a single monitor node through a server node. We first consider a Gilbert-Elliot service profile at the server node. In this model, service times at the server node follow a finite state Markov chain with two states: ${bad}$ state $b$ and ${good}$ state $g$ where the server is faster in state $g$. We determine the time average age experienced by the monitor node and characterize the age-optimal state transition matrix $P$ with and without an average cost constraint on the service operation. Next, we consider a Gilbert-Elliot sampling profile at the source. In this model, the interarrival times follow a finite state Markov chain with two states: ${bad}$ state $b$ and ${good}$ state $g$ where samples are more frequent in state $g$. We find the time average age experienced by the monitor node and characterize the age-optimal state transition matrix $P$.
This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel. Considering a Gilbert- Elliot channel model, information freshness is evaluated through a penalty
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 experien
Given $n$ randomly located source-destination (S-D) pairs on a fixed area network that want to communicate with each other, we study the age of information with a particular focus on its scaling as the network size $n$ grows. We propose a three-phase
We consider the age of information in a multihop multicast network where there is a single source node sending time-sensitive updates to $n^L$ end nodes, and $L$ denotes the number of hops. In the first hop, the source node sends updates to $n$ first
In this paper, we consider a multiuser uplink status update system, where a monitor aims to timely collect randomly generated status updates from multiple end nodes through a shared wireless channel. We adopt the recently proposed metric, termed age