A Cross-Layer FL-Based Clustering Protocol to Support Multicast Routing in IoT-Enabled MANETs With CF-mMIMO

Abstract

This paper proposes a novel cross-layer federated learning (FL)-based clustering (CFLC) protocol to support multicast routing in internet of things (IoT)-enabled mobile ad hoc networks (MANETs) with cell-free massive multiple-input multiple-output (CF-mMIMO). The proposed CFLC protocol leverages cross-layer and FL approaches to enhance network stability and connectivity by optimizing cluster head (CH) selection and cluster formation. The cross-layer design integrates physical layer information, such as mobility (speed and direction), position, channel capacity, and remaining energy, with network layer information (connectivity) to maximize the cost function value for cluster formation. We design the FL model to improve the clustering performance and satisfy future mobile network requirements. Specifically, during the CH selection step, FL can decide which nodes should be elected as CHs or cluster members (CMs) by using classification. In the cluster formation step, FL addresses a regression problem by optimizing the cost function weights for parameters such as mobility similarity, link quality, remaining energy, and channel capacity to decide which CH each node should follow. The simulation results show that the proposed CFLC protocol outperforms the benchmark protocols in terms of connectivity, scalability, and control overhead. Additionally, the results indicate that the CFLC protocol performs particularly well when using the reference point group mobility (RPGM) model, highlighting its advantage over the random waypoint (RWP) mobility model in maintaining network stability and connectivity.