Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication

TL;DR

This work addresses goal-oriented, end-to-end communication by integrating push- and pull-based updates into a unified push-and-pull model and introducing a Grade of Effectiveness GoE metric that captures freshness, timeliness, and usefulness of updates. It formulates a CMDP-based, model-driven framework with dual policies for the sensing and actuation agents, solves it via a dual optimization with an iterative μ-updating algorithm, and demonstrates substantial gains in GoE and energy efficiency over traditional models. The paper also provides model-free reinforcement-learning comparisons and practical threshold-based lookup maps to enable real-time, effect-aware decisions, showing significant benefits when both agents operate with effect-aware strategies. These results highlight the potential of goal-oriented, threshold-driven strategies to improve endpoint actions while reducing unnecessary transmissions, with practical impact for cyber-physical systems and remote monitoring.

Abstract

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain a push-and-pull model, which allows the transmission controller at the SA to decide to push an update to the AA and the query controller at the AA to pull updates by raising queries at specific time instances. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating updates' freshness, usefulness, and timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of updates' effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic effect-agnostic policies at either or both agents.

Figures (16)

• Figure 1: A timing diagram of processes involving the sensing and actuation agents, illustrating interactions and update communications leading to actions.
• Figure 2: The outline of the action and idle windows in different models.
• Figure 3: Time partitioning of the estimation and decision horizons.
• Figure 4: Evolution of the average effectiveness accumulated over time in the push-and-pull model.
• Figure 5: Average update transmission and query rates for different decision policies in the push-and-pull model.

Theorems & Definitions (4)

• Proposition 1
• proof
• Proposition 2
• proof