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

Pietro Talli, Federico Mason, Federico Chiariotti, Andrea Zanella

TL;DR

This work analyzes push- and pull-based communication in cyber-physical systems through a VoI-centered lens, modeling the interaction as a Markov Decision Process where the actuator may incur a cost to obtain state information from a sensor. It develops a model-based framework with a pull-based two-step policy (control and communication) and an alternative push-based two-player Markov game, deriving algorithmic approaches and establishing theoretical properties such as finite convergence in potential games and PPAD-hardness for the optimal joint policy. Numerical results on a 30-state, 4-action MDP show that push-based policies can achieve higher long-term rewards and adapt update frequencies more efficiently, but pull-based policies can be more predictable and, in some regimes, Pareto-dominant for AoI-like criteria. The work also connects Age of Information and Value of Information to quantify timeliness versus control quality, offering guidance for CPS design under communication constraints and highlighting cases where push-based coordination may not be advantageous.

Abstract

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.

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

TL;DR

This work analyzes push- and pull-based communication in cyber-physical systems through a VoI-centered lens, modeling the interaction as a Markov Decision Process where the actuator may incur a cost to obtain state information from a sensor. It develops a model-based framework with a pull-based two-step policy (control and communication) and an alternative push-based two-player Markov game, deriving algorithmic approaches and establishing theoretical properties such as finite convergence in potential games and PPAD-hardness for the optimal joint policy. Numerical results on a 30-state, 4-action MDP show that push-based policies can achieve higher long-term rewards and adapt update frequencies more efficiently, but pull-based policies can be more predictable and, in some regimes, Pareto-dominant for AoI-like criteria. The work also connects Age of Information and Value of Information to quantify timeliness versus control quality, offering guidance for CPS design under communication constraints and highlighting cases where push-based coordination may not be advantageous.

Abstract

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.
Paper Structure (9 sections, 3 theorems, 22 equations, 3 figures, 2 algorithms)

This paper contains 9 sections, 3 theorems, 22 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Markov Decision Processes
  4. Analytical Solution
  5. Pull-based Communication
  6. Push-based Communication
  7. Numerical Evaluation
  8. Age and Value of Information in Effective Communication
  9. Conclusion

Key Result

Theorem 1

The pull-based strategy Pareto dominates the -based policy, i.e., $\bm{\Delta}^*_{\text{pull}}(\beta)\succeq\Delta^*_{\text{AoI}}(\beta)\,\forall\langle \mathcal{S}, \mathcal{A}, \mathbf{P}, r, \gamma,\beta \rangle$.

Figures (3)

  • Figure 1: Figure of the Reward and Communication Cost for different densities and values of $\beta$.
  • Figure 2: Peak AoI distribution for $d=0.1$, $\beta=1$ and sparse reward.
  • Figure 3: Example of a Markov model with $5$ states and $2$ actions.

Theorems & Definitions (8)

  • Definition 1
  • Definition 2
  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof