Risk-Aware Optimization of Age of Information in the Internet of Things

TL;DR

This paper introduces a risk-aware approach to optimize Age of Information in IoT systems by incorporating CVaR, enabling better handling of rare, critical events and providing a computationally feasible solution.

Contribution

It proposes a novel risk-aware MDP framework using CVaR for AoI minimization in IoT, reducing complexity via state augmentation and deriving optimal policies.

Findings

The risk-aware MDP can be solved efficiently using dynamic programming.

The approach effectively minimizes AoI considering rare, high-impact events.

The method is computationally tractable for real-time IoT monitoring.

Abstract

Minimization of the expected value of age of information (AoI) is a risk-neutral approach, and it thus cannot capture rare, yet critical, events with potentially large AoI. In order to capture the effect of these events, in this paper, the notion of conditional value-at-risk (CVaR) is proposed as an effective coherent risk measure that is suitable for minimization of AoI for real-time IoT status updates. In the considered monitoring system, an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status update process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted risk-aware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the risk-aware MDP is reduced to a standard MDP with an augmented state space, for which we derive the optimal stationary policy using dynamic programming. In particular, the optimal history-dependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is shown to be computationally tractable and able to minimize the AoI in real-time IoT monitoring systems in a risk-aware manner.