An Adaptive Multi-Metric Fuzzy Logic-Based Parent Selection Strategy for Scalable RPL Routing in Heterogeneous WSNs

In wireless sensor networks (WSNs), the Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto standard for routing; however, its performance degrades in large-scale, dynamic, or resource-constrained deployments due to unbalanced load distribution, premature node failures, and limited scalability. This paper proposes an adaptive parent selection framework that integrates three critical parameters—residual energy, queue length, and parent capacity—into a unified decision model. A Mamdani-type fuzzy inference system processes these inputs to compute an optimal parent score, while an autoregressive moving average (ARMA) model monitors packet arrival intervals to regulate parent reselection. This ensures that route changes are triggered only by significant traffic deviations, avoiding unnecessary switching and improving stability. Additionally, by incorporatingMAC-layer feedback from IEEE 802.15.4, the method enhances route reliability, balances energy consumption, and reduces retransmissions. Simulation studies over a 500×500 m² region with 100–1000 nodes demonstrate significant improvements compared to baseline RPL and benchmark protocols, achieving 83.91% throughput efficiency, a 46.7% reduction in energy consumption, and extended network lifetime.