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Mobile Edge Computing (MEC), which is the main technology behind 5th Generation (5G) networks, is a nascent paradigm that meets the demands of IoT (Internet of Things) and localized computing. From an end-user point of view, it can be regarded as a refinement of cloud computing and Fog computing. The transfer of computational tasks to the closet MEC servers leads to energy efficiency and low latency. It is important to apply the most suitable policies for scheduling, especially when configuring different modules of an IoT application in MEC. In this paper, three algorithms, namely the heuristic Bald Eagle Search Optimisation [BESO] algorithm, Particle Swarm Optimization algorithm [PSO], and Genetic Algorithm [GA], are presented to carry out heuristic offloading of computational tasks with a view to improving the latency and performance of MEC. However, the most effective algorithm must be adopted to conduct these tasks. As a result, this paper attempts to find an algorithm that is most appropriate for MEC. To demonstrate this. the three algorithms were tested in the Long-Term Evolution [LTE] based Orthogonal frequency-division multiplexing [OFDM] network during a period when the edge nodes had no adequate resources. The performance and efficiency of the three algorithms, BESO, PSO and GA, were determined and compared. After generating the results, the comparative results were evaluated. In terms of offloading the computational tasks, the BESO algorithm was discovered to perform better, with greater energy efficiency and lower latency, than the other two algorithms.

The fifth generation (5G) network technology has made it possible to further exploit the radio spectrum and allow a large number of devices to concurrently have access to the mobile internet. The network has become a viable option for creating such connections and providing Internet of Things (IoT) services in a fast, secure and reliable way. In micro cloud computing, Edge computing refers to a term that describes the edge computing technology. It is expected that the IoT would utilize edge computing to minimize offloading tasks and latency as well as use the computing power in the offloading process. Although edge computing is an old technology, its role in facilitating the real-time transfer of data from devices to the cloud and the real-time processing of data within the devices has only been realized in recent times. In the process of offloading data and computing tasks, the data flow may be interrupted. The 5G technology can provide a better solution to help IoT applications close the gap between edge and the limited device resources, thus making it more reliable. This paper employs the Bald eagle search (BES) algorithm, particle swarm optimization algorithm, and genetic algorithm to simulate the edge computing. The goal is to determine which of the algorithms has the best performance, based on their latency and offloading capacity, in edge computing by comparing their results. On the basis of the execution time, amount of resources utilized in offloading tasks, and total cost of vehicular edge, the three algorithms are compared and validated. According to the simulation results, the best-performing method is the BES algorithm, as it gives the IoT quick access to information.