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

TL;DR

This work addresses the challenge of delivering fresh and meaningful information from energy-harvesting IoT sensors by introducing the Query Version AoI (QVAoI) metric, which jointly accounts for freshness, relevance, and value in a pull-based setting. The authors formulate an infinite-horizon average-cost MDP to optimize QVAoI (and related metrics) under stochastic energy, version generation, and query arrivals, proving a threshold structure for the optimal policy and deriving a closed-form expression for the average QVAoI in the single-sized-battery case. Through Relative Value Iteration, they compare QVAoI-Optimal, QAoI-Optimal, VAoI-Optimal, and AoI-Optimal policies against a greedy baseline, showing significant gains in both freshness and usefulness, and in some regimes achieving the same performance with fewer transmissions. The findings demonstrate that semantics-aware policies can reduce transmissions while maintaining or improving information value, offering practical energy efficiency benefits for EH IoT systems and guiding the design of next-generation status-update networks.

Abstract

In this work, we study the freshness and significance of information in an IoT status update system where an Energy Harvesting (EH) device samples an information source and forwards the update packets to a destination node through 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). By employing the MDP framework, we formulate the optimization problem and determine the optimal transmission policies at the device, which involve deciding the time slots for updating, subject to the energy limitations imposed by the device's battery and energy arrivals. Through analytical and numerical results, we compare the performance of QVAoI-Optimal, QAoI-Optimal, VoI-Optimal, and AoI-Optimal policies with a baseline greedy policy. All semantics-aware policies show significantly improved performance compared to the greedy policy. The QVAoI-Optimal policy, in particular, demonstrates a significant performance improvement by either providing fresher, more relevant, and valuable updates with the same amount of energy arrivals or reducing the number of transmissions in the system to maintain the same level of freshness and significance of information compared to the QAoI-Optimal and other policies.

Figures (18)

• Figure 1: Status update system for various applications. In pull-based setups, the receiver requests the data when needed by sending queries, whereas in push-based setups, the transmitter forwards data to the receiver regardless of the queries.
• Figure 2: Evolution of metrics over time.
• Figure 3: The considered system model.
• Figure 4: The resulting Markov chain with threshold $\Delta_T = 0$ (greedy policy).
• Figure 5: The resulting Markov chain with threshold $\Delta_T = 1$.

Theorems & Definitions (12)

• Definition 1
• Proposition 1
• proof
• Proposition 2
• proof
• Definition 2
• Theorem 1
• proof
• Theorem 2
• proof