Energy-Efficient Internet of Things-Based Wireless Sensor Networks through AI-Powered Resource Allocation

Abstract

Adaptable, energy-efficient, and data-optimized environments are now achievable through the integration of wireless sensor networks (WSNs) and the Internet of Things (IoT). However, challenges such as resource constraints, high energy consumption, and inefficient communication protocols limit their effectiveness. To address these issues, this study employs the Whale Optimization Algorithm (WOA) in an AI-based approach. The proposed method optimizes resource allocation by minimizing communication costs, balancing network loads, and enhancing energy efficiency. Experimental results demonstrate significant improvements in network lifetime, throughput, and spectral efficiency. These findings highlight the effectiveness of AI-driven techniques in significantly boosting IoT network reliability, longevity, and resilience against operational challenges. By leveraging intelligent optimization, the proposed solution not only enhances WSN performance in IoT applications but also streamlines resource allocation and energy efficiency. This makes it a promising approach for next-generation smart homes, industrial IoT environments, healthcare monitoring, and other IoT-driven systems. The study underscores the substantial potential of AI-based optimization in overcoming key limitations of WSNs, paving the way for more sustainable, scalable, and efficient IoT deployments across diverse applications.