A New QoS Optimization in IoT-Smart Agriculture Using Rapid-Adaption-Based Nature-Inspired Approach

The rapid growth of the Internet of Things (IoT) in the early 21st century has introduced complexities in delivering various services, including Quality-of-Service (QoS) management for smart agriculture sensors. Selecting optimal IoT nodes considering QoS parameters, such as energy consumption, latency, and network coverage area has become challenging. In response, this research proposes an extended form of differential evolution (DE) that incorporates a rapid adaptation approach using optimization-based design. By leveraging dynamic information from IoT devices, the proposed approach enhances exploration and exploitation capabilities, allowing for adaptive adjustment of algorithm parameters and strategies. Additionally, a novel fitness function for energy harvesting in IoT-based applications is introduced. The effectiveness of the proposed algorithm is evaluated in IoT-based applications and an IoT-service framework, with comparative analysis against state-of-the-art algorithms. The results demonstrate that the proposed approach achieves superior performance in energy harvesting QoS, delay, service cost, and maximum coverage area in the IoT-service network. This research contributes to the IoT field by offering an advanced DE algorithm that addresses limitations, providing valuable insights for QoS management in IoT-based services, particularly in the context of smart agriculture sensors.