Hybrid optimization algorithm for resource-efficient and data-driven performance in agricultural IoT

Abstract

The agricultural sector is undergoing a significant transformation with the adoption of the agricultural internet of things (IoT), yet it faces persistent challenges in optimizing resource efficiency and data-driven performance due to limitations in current optimization algorithms. This research assesses the effectiveness of four prominent algorithms such as ant colony optimization (ACO), genetic algorithms (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) in addressing these challenges within agricultural IoT (AIoT). Introducing a novel hybrid optimization algorithm (HOA), we aim to overcome these limitations by prioritizing both resource efficiency and data-driven performance. Through a thorough evaluation, HOA demonstrates its superiority in enhancing both aspects, thereby establishing itself as a compelling solution for AIoT applications. The introduction of HOA sets the stage for sustainable, cost-effective, and data-driven precision agriculture, significantly enhancing resource efficiency and data accuracy within the IoT network.