Adaptive Task Scheduling for IoT Devices Using Improved Genetic Algorithms in Mobile Edge Computing
DOI:
https://doi.org/10.37628/ijmet.v9i2.1962Abstract
In order to tackle the growing complexity of intelligent services while staying within the resource limitations of mobile devices, this research explores how to optimize task scheduling in the context of mobile edge computing. The study's proposal of a new algorithm that improves computational efficiency and energy utilization in devices enabled by the Internet of Things (IoT) becomes more relevant as the IoT grows in popularity. This approach maximizes computing tasks while also aligning with sustainable energy practices. This is especially important for IoT devices, as their battery life is often constrained. In this study, a complex offloading mechanism is used to dynamically distribute computation tasks from IoT devices to mobile edge computing (MEC) servers,
rather than to local processing. Internet of Things (IoT) ecosystems benefit greatly from this mechanism because devices in these environments often perform data-intensive tasks. Using an Improved Genetic Algorithm (IGA) to
further optimize the proposed algorithm over the conventional Genetic Algorithm (GA), the algorithm takes a hybrid approach, combining priority scheduling and the shortest job first (SJF) strategy. This study provides a comprehensive simulation analysis that demonstrates how to reduce execution time and energy consumption while efficiently allocating tasks with different priorities and computational demands. Ultimately, this study makes a major contribution to mobile edge computing, which has important consequences for IoT networks. The suggested algorithm promotes a sustainable approach to energy consumption while simultaneously supporting the operational demands of IoT devices through efficient task scheduling and energy management. With this work as a springboard, researchers can investigate how these algorithms can be fine-tuned and modified to suit the changing demands of computing paradigms and the Internet of Things (IoT).
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