Adaptive Peer Clustering with Hierarchical Random Linear Network Coding for Resilient Decentralized Wireless Networks
2026-08-26 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster ComputingInformation TheoryNetworking and Internet Architecture
AI summaryⓘ
The authors study a way to improve communication in groups of wireless devices like cars or IoT sensors that move and connect unpredictably. They propose a method called APC-RLNC, which groups devices based on how reliable their connections are and uses a special coding technique within and between these groups to reduce data loss. Their approach adapts over time and performs better than existing methods in tests involving fast-moving networks and interference. The system works well even with hundreds of devices and keeps delays low. This work helps create more reliable, flexible wireless networks for future technologies.
Random Linear Network CodingWireless networksPacket delivery ratioVehicular networksEdge AIMarkov erasure channelsNetwork clusteringFollow-the-Regularized-Leader algorithmLatencyOnline learning
Authors
Navaneetha Krishnan Kamalakannan, Harinisri Velmurugan
Abstract
Decentralized wireless collectives including vehicular swarms, IoT clusters, and edge AI networks require communication protocols that maintain robustness under dynamic topologies and heterogeneous link quality. While Random Linear Network Coding (RLNC) provides algebraic resilience against packet erasures, its performance degrades significantly when peers exhibit diverse channel conditions. This paper presents Adaptive Peer Clustering with Hierarchical RLNC (APC-RLNC), a system that dynamically groups peers by exponentially weighted moving average (EWMA) reliability metrics and applies multi-tier network coding within and across clusters. We formalize the clustering optimization problem, derive closed-form decoding probability bounds for Markov erasure channels, and prove O(sqrt(T)) regret for online reconfiguration under the Follow-the-Regularized-Leader (FTRL) framework. Our implementation includes both a high-fidelity network simulator and a proof-of-concept testbed deployment on Jetson Nano edge devices. Evaluation across diverse scenarios including high-mobility vehicular networks, burst-error channels, and adversarial interference demonstrates 5.2-9.8 percentage-point packet delivery ratio (PDR) improvements, 10-23% latency reductions, and up to 30% higher node retention compared to state-of-the-art baselines. The system exhibits linear scalability to 500+ nodes and maintains real-time reconfiguration overhead below 3%. APC-RLNC establishes adaptive clustering as a foundational primitive for AI-native 6G wireless systems.