Machine Learning‐Based Routing Framework for WSNs Using Learning Automata and Genetic Algorithms

WSNs play a pivotal role in enabling ubiquitous data collection in many areas including environmental monitoring, smart infrastructure, and industrial automation. Despite their benefits, WSNs are limited by the scarcity of energy sources and are extremely vulnerable to link failures, buffer overflow, and random SN failures. Such problems tend to cause more packet loss, transmission delays, and shorter network life. To resolve these concerns, this work proposes a smart hybrid routing framework, which is the combination of Learning Automata (LA), Genetic Algorithms (GA), and Machine Learning (ML) to achieve reliable data delivery and enhance the energy efficiency of the network. In the initial phase, LA is applied to create a context‐sensitive population of candidate routes based on the SN's parameters like proximity, residual energy, link quality, and buffer occupancy. Furthermore, GA is used to optimize these paths as directed by a multiparameter fitness function. This framework is uniquely designed as it employs ML in the routing process to achieve: (i) predictive link quality estimation with supervised learning, (ii) predicting energy depletion and buffer congestion with time‐series models, (iii) dynamic adaptation of fitness function weights with reinforcement learning, and (iv) anomaly detection with unsupervised learning to isolate unstable or compromised SNs. Simulation results also verify that the proposed ML‐enhanced LA‐GA framework is energy efficient, optimizes the delay in packet delivery, diminishes the retransmission overhead, as well as boosts the network life when compared to the traditional routing schemes based on GA.