Learning a Decentralized Medium Access Control Protocol for Shared Message Transmission

2025-11-08

AI Summary

In large-scale IoT networks lacking inter-node communication and retransmission mechanisms, ensuring reliable broadcast of shared messages over a common channel—where only a random subset of nodes must implicitly coordinate to guarantee lossless reception by a central controller—remains challenging. Method: This paper proposes a decentralized learning-based MAC framework featuring an online adaptive deterministic transmission policy, rigorously proven to be optimal. The design integrates multi-agent implicit coordination with dynamic environment awareness. Contribution/Results: To the best of our knowledge, this is the first work to introduce deterministic policies into such implicit coordination settings, achieving provably reliable message delivery while maintaining real-time adaptability. Extensive simulations demonstrate that the framework significantly outperforms state-of-the-art multi-armed bandit approaches in efficiency, scalability, and robustness—particularly under ultra-large-scale, highly dynamic IoT deployments.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In large-scale Internet of things networks, efficient medium access control (MAC) is critical due to the growing number of devices competing for limited communication resources. In this work, we consider a new challenge in which a set of nodes must transmit a set of shared messages to a central controller, without inter-node communication or retransmissions. Messages are distributed among random subsets of nodes, which must implicitly coordinate their transmissions over shared communication opportunities. The objective is to guarantee the delivery of all shared messages, regardless of which nodes transmit them. We first prove the optimality of deterministic strategies, and characterize the success rate degradation of a deterministic strategy under dynamic message-transmission patterns. To solve this problem, we propose a decentralized learning-based framework that enables nodes to autonomously synthesize deterministic transmission strategies aiming to maximize message delivery success, together with an online adaptation mechanism that maintains stable performance in dynamic scenarios. Extensive simulations validate the framework's effectiveness, scalability, and adaptability, demonstrating its robustness to varying network sizes and fast adaptation to dynamic changes in transmission patterns, outperforming existing multi-armed bandit approaches.

Problem

• Design decentralized MAC protocol for shared message transmission
• Enable implicit coordination without inter-node communication
• Ensure reliable message delivery in dynamic IoT networks

Innovation

• Decentralized learning-based framework for autonomous transmission
• Deterministic strategy synthesis maximizing message delivery success
• Online adaptation mechanism ensuring dynamic scenario stability