Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks

TL;DR

This paper introduces a novel mean-field game approach to enhance backpressure-based scheduling in multi-hop wireless sensor networks, enabling decentralized decision-making that balances local queue states with global network behavior.

Contribution

It extends traditional backpressure algorithms by integrating mean-field effects, allowing for scalable and efficient decentralized scheduling in large-scale WSNs.

Findings

Improved congestion management in large-scale networks

Enhanced scheduling efficiency through mean-field integration

Numerical results confirm better network stability

Abstract

We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as a performance criterion. Traditional backpressure algorithms leverage queue differentials to regulate data flow and maintain network stability. In this work, we extend the backpressure framework by incorporating a mean-field term into the cost functional, capturing the global behavior of the system alongside local dynamics. The resulting model utilizes the strengths of non-cooperative mean-field type games, enabling nodes to make decentralized decisions based on both individual queue states and system mean-field effects while accounting for stochastic network interactions. By leveraging the interplay between backpressure dynamics and mean-field coupling, the approach balances local optimization with global efficiency. Numerical simulations demonstrate the efficacy of the proposed method in handling congestion and scheduling in large-scale WSNs.