Abstract

The growing demand for efficient and reliable sensor networks in applications such as environmental monitoring, healthcare, and smart cities has highlighted the need for optimizing communication protocols, power consumption, and data management. Traditional methods often focus on optimizing individual layers of the network without considering the interactions across layers. This approach limits the overall performance, especially as networks scale in size and complexity. The lack of effective cross-layer optimization strategies hinders the performance of sensor networks, leading to suboptimal energy usage, low throughput, and high latency. Moreover, sensor nodes in such networks often face constraints such as limited energy, computation power, and memory, making traditional optimization methods inadequate for achieving high efficiency across all layers of the network. This paper proposes a cross-layer design and optimization framework leveraging Artificial Intelligence (AI) and Deep Learning (DL) algorithms, specifically Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN), to address these challenges. The RNN is employed to model the temporal dependencies in the sensor data, while the DBN is used for optimizing decision-making processes across multiple network layers. The proposed framework dynamically adjusts routing, data aggregation, and power control parameters based on real-time conditions, improving overall network performance. Simulation results demonstrate that the proposed approach outperforms traditional cross-layer design methods. The RNN-DBN framework achieved a 35% improvement in energy efficiency, a 25% reduction in latency, and a 40% increase in data throughput compared to existing optimization techniques. These enhancements are particularly significant in large-scale, real-time sensor networks.