Self-Evolving Deep Neural Networks for Continuous Learning in Dynamic IoT Environments

Abstract

The explosive growth of Internet of Things (IoT) networks has introduced unprecedented levels of data diversity, environmental volatility, and rapid concept drift. Traditional deep neural networks (DNNs), trained in static and centralized settings, are ill-equipped to adapt continuously to evolving IoT data streams without retraining from scratch or suffering catastrophic forgetting. To address these limitations, this paper proposes a novel Self-Evolving Deep Neural Network (SE-DNN) framework designed for real-time, lifelong learning in dynamic IoT ecosystems. The architecture autonomously expands, prunes, and restructures itself based on incoming data complexity, task demands, and temporal drift patterns. SE-DNN integrates adaptive layer growth, neuron evolution, memory-augmented attention, and drift detection mechanisms that identify distributional shifts and trigger structural updates. A hybrid learning strategy combining online incremental learning, episodic memory replay, and meta-learning ensures stability–plasticity balance, enabling the model to learn new patterns without forgetting previously acquired knowledge. Experimental evaluations on large-scale IoT datasets—including sensor streams, anomaly detection, smart grid signals, and industrial monitoring—demonstrate that the proposed SE-DNN achieves superior adaptability, higher accuracy under drift, reduced retraining cost, and lower model degradation compared to conventional DNNs and existing continual learning baselines. The findings affirm SE-DNN as a scalable, autonomous, and evolution-capable deep learning paradigm highly suited for next-generation IoT environments characterized by continuous change and real-time operational demands.