Hybrid Genetic Algorithm-Based Critical Node Identification for Enhanced RPL Objective Function in Iot Networks
Abstract
The Routing Protocol for Low-Power and Lossy Networks (RPL) remains the de facto IPv6 routing standard for constrained Internet of Things (IoT) deployments. However, its default objective functions, Objective Function Zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF), rely on isolated single-metric evaluations or rigid metric combinations. Consequently, in dense deployments characterized by node mobility and high traffic volumes, these conventional mechanisms often fail to optimize parent selection. Although multi-metric objective functions—incorporating fuzzy logic and entropy-based designs—have demonstrated that evaluating link quality, residual energy, and node stability simultaneously improves routing performance, many existing schemes rely on pre-deployed static weights or neglect application-specific context during parent selection. To bridge this gap, this paper proposes an enhanced objective function framework that introduces the concept of graph-theoretical critical nodes, offering greater adaptability in defining routing topologies. By identifying nodes whose high placement within the Destination-Oriented Directed Acyclic Graph (DODAG) would be detrimental to overall network performance, we dynamically regulate their topological hierarchy. Specifically, we model the network as an undirected graph and apply a hybrid genetic algorithm to solve the 3-Component Critical Node Problem (3C-CNP). The resulting critical node set serves as an input for two proposed parent selection algorithms designed to demote or exclude critical nodes, thereby minimizing their adverse impact on routing stability. Extensive Contiki-NG simulations demonstrate that Variant 2 reduces parent churn by a factor of 3 to 4, lowers energy consumption by 35\% to 40\%, and significantly extends network lifetime under high traffic stress compared to standard OF0, achieving a superior balance between stability and efficiency.References
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DOI:
https://doi.org/10.31449/inf.v50i15.15528Keywords:
RPL , Objective function , Critical nodes , Parent selection , 3C-CNPDownloads
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