Abstract

To enhance the efficiency of edge-integrated Industrial IoT (IIoT) networks, this paper proposes a deep learning-based resource-scheduling framework for optimized asset booking in Wireless Sensor Networks (WSNs). The novelty of this work lies in the integration of a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model, which enables intelligent allocation of computational resources based on real-time asset demand characteristics. The proposed model is evaluated using the Intel Berkeley WSN dataset and demonstrates superior performance in terms of latency reduction, execution time, and resource utilization compared to conventional approaches such as Genetic Algorithm (GA), Improved Particle Swarm Optimization (IPSO), Long Short-Term Memory (LSTM), and Bidirectional Recurrent Neural Network (BRNN). With a maximum efficiency of 99.48% and the lowest observed average delay, the model proves effective for real-time industrial automation scenarios. This research contributes to the development of scalable, energy-efficient, and responsive WSN architectures by leveraging deep learning for asset booking in edge-IoT environments.