Energy Aware Deep Learning Workflow for Intelligent Routing and Classification in WSNs Under Green AI Principles

Energy efficiency has become central to the sustenance and manageable growth of communication infrastructure in the advent of Wireless Sensor Networks (WSN) environment. The paradigm of green AI, emerging relatively recently, focuses on designing the models of machine learning and smart workflows to consume the least amount of energy while maintaining performance. Deep learning is such an important aspect in WSN as it allows predictive analytics, smart routing, and adaptive decision-making. But, by disregarding real-time energy-conscious of energy, it will result in wasteful energy usage, short lifetime and frequent route failure in dynamic networks. To resolve this challenge, the proposed model proposes an optimized deep learning workflow that would be compatible with the principles of Green AI. This is initiated through a soft-attention mechanism of Energy-Aware Attention-Based Neighbor Discovery (EA-AND) that ranks neighboring nodes via residual energy, link stability as well as relative distances, and only the communication-optimal nodes are forwarded to downstream routines. Based on the determined neighborhood, Energy-Efficient Cluster Routing (EECR) calculates a score of cluster head suitability using attention values, the normalization of energy and delay-sensitive link costs and achieves well-rounded cluster head formation, which saves unwanted energy consumption. The model is followed with Social Spider Optimization (SSO) to find the optimal subset of features to process data, a vibration inspired metaheuristic approach in which subsets are evaluated in terms of energy consumption, accuracy of classification and redundancy reduction that does provide a way to reduce computation overhead but also to retain relevant information. In the node classification task, a new deep learning model based on Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) is used to learn the node behavior and the sensors' readings spatial dependencies and temporal patterns, promoting the robustness of the classification and finding faults. In addition to the workflow, a Cooperative Energy-Aware Preemptive Route Scheduling (CE-APRS) mechanism adapts routing paths predicted fail- prone nodes and energy limits proactively to prevent breakages and proactive load balancing. The suggested model shows considerable enhancements in energy-aware learning, and intelligent decision-making in resource-limited wireless networks.