# Push- and Pull-based Effective Communication in Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2401.10921 · HTML: https://mlsanity.com/paper/2401.10921/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games](https://mlsanity.com/paper/2109.01795/) (2021)
- [Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference](https://mlsanity.com/paper/2306.17580/) (2023)
- [Multi-Agent Reinforcement Learning for Pragmatic Communication and Control](https://mlsanity.com/paper/2302.14399/) (2023)

## Built on by (most cited first, 2 of 2)

- [Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding](https://mlsanity.com/paper/2508.07722/) (2025)
- [Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks](https://mlsanity.com/paper/2601.01179/) (2026)

## Abstract (arXiv)

In Cyber Physical Systems (CPSs), two groups of actors interact toward the maximization of system performance: the sensors, observing and disseminating the system state, and the actuators, performing physical decisions based on the received information. While it is generally assumed that sensors periodically transmit updates, returning the feedback signal only when necessary, and consequently adapting the physical decisions to the communication policy, can significantly improve the efficiency of the system. In particular, the choice between push-based communication, in which updates are initiated autonomously by the sensors, and pull-based communication, in which they are requested by the actuators, is a key design step. In this work, we propose an analytical model for optimizing push- and pull-based communication in CPSs, observing that the policy optimality coincides with Value of Information (VoI) maximization. Our results also highlight that, despite providing a better optimal solution, implementable push-based communication strategies may underperform even in relatively simple scenarios.