38 research outputs found
Age-Optimal Updates of Multiple Information Flows
In this paper, we study an age of information minimization problem, where
multiple flows of update packets are sent over multiple servers to their
destinations. Two online scheduling policies are proposed. When the packet
generation and arrival times are synchronized across the flows, the proposed
policies are shown to be (near) optimal for minimizing any time-dependent,
symmetric, and non-decreasing penalty function of the ages of the flows over
time in a stochastic ordering sense
Implementation of Distributed Time Exchange Based Cooperative Forwarding
In this paper, we design and implement time exchange (TE) based cooperative
forwarding where nodes use transmission time slots as incentives for relaying.
We focus on distributed joint time slot exchange and relay selection in the sum
goodput maximization of the overall network. We formulate the design objective
as a mixed integer nonlinear programming (MINLP) problem and provide a
polynomial time distributed solution of the MINLP. We implement the designed
algorithm in the software defined radio enabled USRP nodes of the ORBIT indoor
wireless testbed. The ORBIT grid is used as a global control plane for exchange
of control information between the USRP nodes. Experimental results suggest
that TE can significantly increase the sum goodput of the network. We also
demonstrate the performance of a goodput optimization algorithm that is
proportionally fair.Comment: Accepted in 2012 Military Communications Conferenc
Age Optimum Sampling in Non-Stationary Environment
In this work, we consider a status update system with a sensor and a
receiver. The status update information is sampled by the sensor and then
forwarded to the receiver through a channel with non-stationary delay
distribution. The data freshness at the receiver is quantified by the
Age-of-Information (AoI). The goal is to design an online sampling strategy
that can minimize the average AoI when the non-stationary delay distribution is
unknown. Assuming that channel delay distribution may change over time, to
minimize the average AoI, we propose a joint stochastic approximation and
non-parametric change point detection algorithm that can: (1) learn the optimum
update threshold when the delay distribution remains static; (2) detect the
change in transmission delay distribution quickly and then restart the learning
process. Simulation results show that the proposed algorithm can quickly detect
the delay changes, and the average AoI obtained by the proposed policy
converges to the minimum AoI