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Effective Communication: When to Pull Updates?

Pouya Agheli, Nikolaos Pappas, Petar Popovski, Marios Kountouris

TL;DR

This work addresses goal-oriented pull-based communication by introducing Grade of Effectiveness ($GoE$), a composite metric combining AoI freshness and update usefulness to quantify the impact of updates at the endpoint. It develops a CMDP framework where a query controller decides when to pull updates based on the evolving source state and the expected usefulness, while constraining the average communication cost. An iterative algorithm solves the dual problem to obtain a $\mu$-optimal policy $\pi_\mu^*$ and the corresponding $\mu^*$, with the optimal policy converging to a practical threshold-based rule. Simulation results show that the proposed effect-aware policy significantly outperforms effect-agnostic baselines in long-term $GoE$, albeit with higher transmission rate, and reveal convergent behavior and interpretable threshold boundaries. The approach offers a scalable, just-in-time querying mechanism for enhancing effectiveness in goal-oriented, information-pull systems.

Abstract

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals so as to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the long-term average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.

Effective Communication: When to Pull Updates?

TL;DR

This work addresses goal-oriented pull-based communication by introducing Grade of Effectiveness (), a composite metric combining AoI freshness and update usefulness to quantify the impact of updates at the endpoint. It develops a CMDP framework where a query controller decides when to pull updates based on the evolving source state and the expected usefulness, while constraining the average communication cost. An iterative algorithm solves the dual problem to obtain a -optimal policy and the corresponding , with the optimal policy converging to a practical threshold-based rule. Simulation results show that the proposed effect-aware policy significantly outperforms effect-agnostic baselines in long-term , albeit with higher transmission rate, and reveal convergent behavior and interpretable threshold boundaries. The approach offers a scalable, just-in-time querying mechanism for enhancing effectiveness in goal-oriented, information-pull systems.

Abstract

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals so as to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the long-term average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.
Paper Structure (17 sections, 1 theorem, 9 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 17 sections, 1 theorem, 9 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Query Control Policies
  4. Effect-agnostic policy
  5. Effect-aware policy
  6. Grade of Effectiveness Metric
  7. GoE Formulation
  8. The Pull-Based System's GoE
  9. Query Control
  10. Problem Formulation
  11. Dual problem
  12. Iterative Algorithm
  13. Computing $\pi_\mu^*$
  14. Computing $\mu^*$
  15. Threshold-Based Query Control Model
  16. ...and 2 more sections

Key Result

Proposition 1

The modeled CMDP pursues the weak accessibility (WA) condition.

Figures (4)

  • Figure 1: The normalized effectiveness performance of different query control policies within $N=50$ slots.
  • Figure 2: The CDF of the average NGoE provided over $N=1000$ time slots.
  • Figure 3: The interplay between average NGoE and query rate for different query control policies and cost coefficients (a) $c_0=0.1$ and (b) $c_0=0.5$.
  • Figure 4: The expected utility obtained in each iteration of Algorithm \ref{['Alg1']}.

Theorems & Definitions (2)

  • Proposition 1
  • proof