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

• URL: http://arxiv.org/abs/2407.14092v1
• Date: Fri, 19 Jul 2024 07:57:31 GMT
• Title: Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication
• Authors: Pouya Agheli, Nikolaos Pappas, Petar Popovski, Marios Kountouris,
• Abstract summary: We consider an end-to-end status update system where a sensing agent observes a source, generates and transmits updates to an actuation agent. We integrate the push- and pull-based update communication models to obtain a push-and-pull model. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates.
• Score: 40.57990979803115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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