12 research outputs found
Optimal Scheduling Policy for Minimizing Age of Information with a Relay
We consider IoT sensor network where multiple sensors are connected to
corresponding destination nodes via a relay. Thus, the relay schedules sensors
to sample and destination nodes to update. The relay can select multiple
sensors and destination nodes in each time. In order to minimize average
weighted sum AoI, joint optimization of sampling and updating policy of the
relay is investigated. For errorless and symmetric case where weights are
equally given, necessary and sufficient conditions for optimality is found.
Using this result, we obtain that the minimum average sum AoI in a closed-form
expression which can be interpreted as fundamental limit of sum AoI in a single
relay network. Also, for error-prone and symmetric case, we have proved that
greedy policy achieves the minimum average sum AoI at the destination nodes.
For general case, we have proposed scheduling policy obtained via reinforcement
learning.Comment: 30 page
Timely Monitoring of Dynamic Sources with Observations from Multiple Wireless Sensors
Age of Information (AoI) has recently received much attention due to its
relevance in IoT sensing and monitoring applications. In this paper, we
consider the problem of minimizing the AoI in a system in which a set of
sources are observed by multiple sensors in a many-to-many relationship, and
the probability that a sensor observes a source depends on the state of the
source. This model represents many practical scenarios, such as the ones in
which multiple cameras or microphones are deployed to monitor objects moving in
certain areas. We formulate the scheduling problem as a Markov Decision
Process, and show how the age-optimal scheduling policy can be obtained. We
further consider partially observable variants of the problem, and devise
approximate policies for large state spaces. Our evaluations show that the
approximate policies work well in the considered scenarios, and that the fact
that sensors can observe multiple sources is beneficial, especially when there
is high uncertainty of the source states.Comment: Submitted for publicatio
A Reinforcement Learning Framework for Optimizing Age-of-Information in RF-powered Communication Systems
In this paper, we study a real-time monitoring system in which multiple
source nodes are responsible for sending update packets to a common destination
node in order to maintain the freshness of information at the destination.
Since it may not always be feasible to replace or recharge batteries in all
source nodes, we consider that the nodes are powered through wireless energy
transfer (WET) by the destination. For this system setup, we investigate the
optimal online sampling policy (referred to as the age-optimal policy) that
jointly optimizes WET and scheduling of update packet transmissions with the
objective of minimizing the long-term average weighted sum of
Age-of-Information (AoI) values for different physical processes (observed by
the source nodes) at the destination node, referred to as the sum-AoI. To solve
this optimization problem, we first model this setup as an average cost Markov
decision process (MDP). Due to the extreme curse of dimensionality in the state
space of the formulated MDP, classical reinforcement learning algorithms are no
longer applicable to our problem. Motivated by this, we propose a deep
reinforcement learning (DRL) algorithm that can learn the age-optimal policy in
a computationally-efficient manner. We further characterize the structural
properties of the age-optimal policy analytically, and demonstrate that it has
a threshold-based structure with respect to the AoI values for different
processes. We extend our analysis to characterize the structural properties of
the policy that maximizes average throughput for our system setup, referred to
as the throughput-optimal policy. Afterwards, we analytically demonstrate that
the structures of the age-optimal and throughput-optimal policies are
different. We also numerically demonstrate these structures as well as the
impact of system design parameters on the optimal achievable average weighted
sum-AoI
Optimizing the Age-of-Information for Mobile Users in Adversarial and Stochastic Environments
We study a multi-user downlink scheduling problem for optimizing the
freshness of information available to users roaming across multiple cells. We
consider both adversarial and stochastic settings and design scheduling
policies that optimize two distinct information freshness metrics, namely the
average age-of-information and the peak age-of-information. We show that a
natural greedy scheduling policy is competitive with the optimal offline policy
in the adversarial setting. We also derive fundamental lower bounds to the
competitive ratio achievable by any online policy. In the stochastic
environment, we show that a Max-Weight scheduling policy that takes into
account the channel statistics achieves an approximation factor of for
minimizing the average age of information in two extreme mobility scenarios. We
conclude the paper by establishing a large-deviation optimality result achieved
by the greedy policy for minimizing the peak age of information for static
users situated at a single cell.Comment: arXiv admin note: text overlap with arXiv:2001.0547