AFHO-DL: Enhancing Energy Efficiency through Resource Allocation AI-enabled WSNs and IoT Integration
S. A. Kalaiselvan, J. Manoranjini, S. Hemalatha, M. Lenin Kumar · 2024
In the realm of AI-enabled Wireless Sensor Networks (WSNs) and Internet of Things (IoT) integration, efficient resource allocation is paramount for enhancing energy efficiency and optimizing data utilization. The dynamic nature of environments poses challenges to achieving optimal performance measures, necessitating sophisticated solutions. To address this, we propose the AFHO-DL model, which combines the Fire Hawk Optimizer (FHO), UNet architecture, and Multi-objective Jaya algorithm. The FHO algorithm, inspired by the foraging behavior of Fire Hawks, efficiently optimizes resource allocation in WSNs by iteratively updating node positions based on objective functions. Leveraging FHO, we fine-tune the parameters of the UNet architecture, a deep learning model widely used in image processing applications. The UNet architecture, consisting of encoder and decoder paths, extracts features and reconstructs images, enhancing adaptability to various data types. Furthermore, the Multi-objective Jaya algorithm is employed to refine solutions by striking a balance between exploration and exploitation in the solution space, further improving resource allocation strategies. Our experimental results demonstrate the effectiveness of the proposed AFHO-DL model in enhancing energy efficiency and optimizing data utilization in dynamic environments. Through post-processing and visualization, we evaluate the performance of the optimization algorithms, generating Pareto-optimal solutions that represent the best trade-offs between competing goals. The AFHO-DL model facilitates intelligent decision-making and resource allocation strategy optimization, ensuring smooth data transfer and improving network efficiency in AI-enabled WSNs coupled with IoT platforms.