The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics

2024-01-03

arXiv.org

AI Summary

This work addresses dynamic update scheduling in energy-constrained wireless status update systems, aiming to jointly mitigate high-risk states characterized by elevated Age of Information (AoI), Query-based AoI (QAoI), and Age of Incorrect Information (AoII). We introduce the novel concept of “risk state”—defined as instances where age metrics exceed critical thresholds—and propose risk-state occurrence frequency as a new risk metric. To optimize update timing under this metric, we design two risk-sensitive strategies: (i) a prior-knowledge-based threshold-triggered policy, and (ii) a model-free enhanced Q-learning algorithm. Leveraging stochastic process modeling and risk-sensitive optimization, our approach achieves joint energy efficiency and risk control without compromising update fidelity. Numerical experiments demonstrate that the proposed methods significantly reduce the frequency of high-risk states while maintaining stringent update quality requirements.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Popular methods to quantify transmitted data quality are the Age of Information (AoI), the Query Age of Information (QAoI), and the Age of Incorrect Information (AoII). We consider these metrics in a point-to-point wireless communication system, where the transmitter monitors a process and sends status updates to a receiver. The challenge is to decide on the best time for an update, balancing the transmission energy and the age-based metric at the receiver. Due to the inherent risk of high age-based metric values causing complications such as unstable system states, we introduce the new concept of risky states to denote states with high age-based metric. We use this new notion of risky states to quantify and minimize this risk of experiencing high age-based metrics by directly deriving the frequency of risky states as a novel risk-metric. Building on this foundation, we introduce two risk-sensitive strategies for AoI, QAoI and AoII. The first strategy uses system knowledge, i.e., channel quality and packet arrival probability, to find an optimal strategy that transmits when the age-based metric exceeds a tunable threshold. A lower threshold leads to higher risk-sensitivity. The second strategy uses an enhanced Q-learning approach and balances the age-based metric, the transmission energy and the frequency of risky states without requiring knowledge about the system. Numerical results affirm our risk-sensitive strategies' high effectiveness.

Problem

• Optimize update timing in wireless systems
• Minimize risk of high age-based metrics
• Develop risk-sensitive strategies for data quality

Innovation

• Introduces risky states for high age-based metrics
• Develops risk-sensitive strategies using system knowledge
• Employs enhanced Q-learning for risk and energy balance