# Pull-Based Query Scheduling for Goal-Oriented Semantic Communication

arXiv: https://arxiv.org/abs/2503.06725 · HTML: https://mlsanity.com/paper/2503.06725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 23 of 23)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach](https://mlsanity.com/paper/2402.07573/) (2024)
- [On the Complexity of Solving Markov Decision Problems](https://mlsanity.com/paper/1302.4971/) (2013)
- [Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks](https://mlsanity.com/paper/1801.01803/) (2018)
- [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019)
- [Age-of-Information vs. Value-of-Information Scheduling for Cellular Networked Control Systems](https://mlsanity.com/paper/1903.05356/) (2019)
- [Query Age of Information: Freshness in Pull-Based Communication](https://mlsanity.com/paper/2105.06845/) (2021)
- [Semantic Filtering and Source Coding in Distributed Wireless Monitoring Systems](https://mlsanity.com/paper/2305.11549/) (2023)
- [Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness](https://mlsanity.com/paper/2306.03750/) (2023)
- [Effective Communication: When to Pull Updates?](https://mlsanity.com/paper/2311.06432/) (2023)
- [Waiting but not Aging: Optimizing Information Freshness Under the Pull Model](https://mlsanity.com/paper/1912.08722/) (2019)
- [Freshness on Demand: Optimizing Age of Information for the Query Process](https://mlsanity.com/paper/2011.00917/) (2020)
- [Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins](https://mlsanity.com/paper/2301.10962/) (2023)
- [Minimizing the Age of Information from Sensors with Common Observations](https://mlsanity.com/paper/1811.06453/) (2018)
- [Goal-Oriented Communication, Estimation, and Control over Bidirectional Wireless Links](https://mlsanity.com/paper/2312.16061/) (2023)
- [Scheduling of Sensor Transmissions Based on Value of Information for Summary Statistics](https://mlsanity.com/paper/2204.12307/) (2022)
- [Optimizing Information Freshness over a Channel that Wears Out](https://mlsanity.com/paper/2312.00914/) (2023)
- [Goal-Oriented Multiple Access Connectivity for Networked Intelligent Systems](https://mlsanity.com/paper/2401.10614/) (2024)
- [Goal-Oriented Sensor Reporting Scheduling for Non-linear Dynamic System Monitoring](https://mlsanity.com/paper/2405.20983/) (2024)
- [Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication](https://mlsanity.com/paper/2407.14092/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving](https://mlsanity.com/paper/2606.28384/) (2026)

## Abstract (arXiv)

This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals at the endpoint. A hub serves as an intermediary, querying the SAs for updates on observed attributes and maintaining a knowledge base, which is then broadcast to the AAs. The AAs leverage the knowledge to perform their actions effectively. To quantify the semantic value of updates, we introduce a grade of effectiveness (GoE) metric. Furthermore, we integrate cumulative perspective theory (CPT) into the long-term effectiveness analysis to account for risk awareness and loss aversion in the system. Leveraging this framework, we compute effect-aware scheduling policies aimed at maximizing the expected discounted sum of CPT-based total GoE provided by the transmitted updates while complying with a given query cost constraint. To achieve this, we propose a model-based solution based on dynamic programming and model-free solutions employing state-of-the-art deep reinforcement learning (DRL) algorithms. Our findings demonstrate that effect-aware scheduling significantly enhances the effectiveness of communicated updates compared to benchmark scheduling methods, particularly in settings with stringent cost constraints where optimal query scheduling is vital for system performance and overall effectiveness.