Goal-Oriented Estimation of Multiple Markov Sources in Resource-Constrained Systems

Conceptos Básicos

The authors investigate goal-oriented communication for remote estimation of multiple Markov sources in resource-constrained networks. They propose policies that minimize the long-term average cost of actuation error subject to resource constraints, utilizing Lyapunov drift techniques and deep reinforcement learning.

Resumen

Goal-oriented communication is crucial for remote estimation in networked control systems. The authors introduce a metric, CAE, to capture state-dependent actuation costs and propose policies that significantly reduce uninformative transmissions while achieving near-optimal performance in CAE minimization.
The study addresses the challenges of under-sampled and delayed measurements in networked control systems. It introduces innovative policies based on Lyapunov drift techniques and deep reinforcement learning to optimize remote estimation processes efficiently.
The proposed DPP policy stabilizes virtual queues while minimizing time-averaged costs, whereas the LO-DRL policy offers real-time decision-making capabilities without prior knowledge of system statistics.
Simulation results demonstrate that the proposed policies outperform baseline strategies, showcasing their effectiveness in reducing average CAE and optimizing transmission frequency under different scenarios with varying numbers of sources.

Estadísticas

ωm ∈ R+
ps = P(ht = 1)
Cmax > 0
V is a non-negative weight

Citas

"The work showcases how semantics-empowered communication can enhance goal-oriented tracking in autonomous systems."
"Information freshness measured by AoI has been employed but does not consider source evolution and application context."
"The proposed DPP policy significantly reduces ineffective status updates while achieving near-optimal performance."

Ideas clave extraídas de

Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems

by Jiping Luo,N... a las arxiv.org 03-05-2024

https://arxiv.org/pdf/2311.07346.pdf

Consultas más profundas

How can the concept of Age of Actuation be applied beyond wireless power transfer systems

The concept of Age of Actuation can be applied beyond wireless power transfer systems in various autonomous systems where timely and accurate information is crucial for decision-making and actuation. For example, in autonomous vehicles, the Age of Actuation metric can be utilized to determine the optimal timing for updating critical system states such as vehicle speed, direction, or proximity to obstacles. By considering the significance of different state updates based on their impact on safe operation, autonomous vehicles can prioritize real-time information flow to ensure timely and effective actuation responses.

What are the implications of prioritizing information flow efficiently based on application demands

Prioritizing information flow efficiently based on application demands has significant implications for enhancing system performance and achieving specific goals. By tailoring communication protocols to consider the semantics and context-aware requirements of messages, systems like swarm robotics or smart factories can optimize resource utilization while minimizing actuation errors. Efficiently prioritizing information flow allows for better decision-making processes by ensuring that critical data reaches its destination promptly when needed most. This approach improves overall system responsiveness, reliability, and adaptability to dynamic environmental conditions.

How can the proposed policies be adapted to address real-world challenges faced by autonomous systems

To address real-world challenges faced by autonomous systems, the proposed policies can be adapted in several ways:

Dynamic Environment Adaptation: Implement mechanisms within the policies that allow them to adapt dynamically to changing environmental conditions or system dynamics.

Integration with Sensor Fusion: Incorporate sensor fusion techniques into policy design to enhance data accuracy and reliability from multiple sources.

Fault Tolerance Mechanisms: Integrate fault tolerance mechanisms into policies to handle unexpected failures or disruptions effectively without compromising system performance.

Edge Computing Capabilities: Enhance policies with edge computing capabilities to enable faster decision-making at the edge level before transmitting data over constrained networks.

By incorporating these adaptations into the proposed policies, autonomous systems can improve their robustness, efficiency, and adaptability in complex operational scenarios while maintaining a focus on goal-oriented estimation under resource constraints.