Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage

TL;DR

This paper introduces a decentralized TDMA slot scheduling protocol for wireless sensor and IoT networks that uses Multi-Armed Bandits for collision avoidance and a novel defragmentation method to optimize bandwidth, adaptable to network changes.

Contribution

It presents a new decentralized framework combining MAB-based slot allocation with DDSB for efficient, adaptive bandwidth management in low-complexity wireless networks.

Findings

Nodes successfully learn collision-free transmission slots using MAB.

The DDSB mechanism reduces bandwidth fragmentation and improves efficiency.

Framework adapts to topological changes while maintaining performance.

Abstract

This paper proposes a Time Division Multiple Access (TDMA) MAC slot allocation protocol with efficient bandwidth usage in wireless sensor networks and Internet of Things (IoTs). The developed protocol has two primary components: a Multi-Armed Bandits (MAB)-based slot allocation mechanism for collision free transmission, and a Decentralized Defragmented Slot Backshift (DDSB) operation for improving bandwidth usage efficiency. The proposed framework is decentralized in that each node finds its transmission schedule independently without the control of any centralized arbitrator. The developed mechanism is suitable for networks with or without time synchronization, thus, making it suitable for low-complexity wireless transceivers for wireless sensor and IoT nodes. This framework is able to manage the trade-off between learning convergence time and bandwidth. In addition, it allows the nodes to adapt to topological changes while maintaining efficient bandwidth usage. The developed logic is tested for both fully-connected and arbitrary mesh networks with extensive simulation experiments. It is shown how the nodes can learn to select collision-free transmission slots using MAB. Moreover, the nodes learn to self-adjust their transmission schedules using a novel DDSB framework in order to reduce bandwidth usage.