The restless multi-armed bandit problem is a paradigmatic modeling framework for optimal
dynamic priority allocation in stochastic models of wide-ranging applications that has been …

In this paper, we aim to design an autonomous tracking system for a swarm of unmanned
aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs …

In this paper, we study a slotted-time system where a base station needs to update multiple
users at the same time. Due to the limited resources, only part of the users can be updated in …

In this paper, we study a sampling and transmission scheduling problem for multi-source
remote estimation, where a scheduler determines when to take samples from multiple …

Restless bandits are a class of sequential resource allocation problems concerned with
allocating one or more resources among several alternative processes where the evolution …

This paper proposes using the uncertainty of information (UoI), measured by Shannon's
entropy, as a metric for information freshness. We consider a system in which a central …

This paper investigates the multi-channel transmission scheduling problem for remote state
estimation based on a hopping scheme in cyber-physical systems. The smart sensor sends …

Reinforcement learning is an attractive approach to learn good resource allocation and
scheduling policies based on data when the system model is unknown. However, the …

This article studies two sensors scheduling with a shared memory channel for remote state
estimation in cyber-physical systems (CPSs). We consider that each sensor monitors a plant …

In this article, we study the problem of user association, ie, determining which base station
(BS) a user should associate with, in a dense millimeter wave (mmWave) network. In our …