From AoI to QVAoI: Query-Based Semantics-Aware Scheduling for Energy-Harvesting IoT Systems

TL;DR

This paper introduces and optimizes a semantics-aware metric, QVAoI, for energy-harvesting IoT systems, improving information freshness and relevance through optimal transmission policies formulated via Markov Decision Processes.

Contribution

It formulates the QVAoI metric and derives optimal transmission policies considering energy constraints, demonstrating significant performance improvements over baseline policies.

Findings

QVAoI-Optimal policy outperforms greedy and other policies in freshness and relevance.

Closed-form expressions for average update rate and QVAoI for a unit-capacity battery.

Semantics-aware policies achieve better performance than greedy policy.

Abstract

In this work, we study the freshness and significance of information in an IoT status update system in which an Energy Harvesting (EH) device samples an information source and forwards update packets to a destination node via a direct channel. We introduce and optimize a semantics-aware metric, Query Version Age of Information (QVAoI), in the system along with other metrics: Query Age of Information (QAoI), Version Age of Information (VAoI), and Age of Information (AoI). We formulate the optimization problem as a Markov Decision Process to determine the optimal transmission policy at the device, which decides the time slots for transmitting updates, subject to the device's battery energy limitations and the energy arrivals. Furthermore, we derive closed-form expressions for the average update rate and the QVAoI for a unit-capacity battery, serving as analytical benchmarks. We compare the performance of QVAoI-Optimal, QAoI-Optimal, VoI-Optimal, and AoI-Optimal policies with a baseline greedy policy. All semantics-aware policies achieve better performance than the greedy policy. The QVAoI-Optimal policy, in particular, demonstrates a significant performance improvement either by providing fresher, more relevant, and more valuable updates with the same energy arrivals or by reducing the number of transmissions in the system while maintaining the same level of freshness and information significance as the QAoI-Optimal and other policies.