Partial Data Offloading with Mobile Edge Computing to Address Joint Challenges in Latency and Energy Efficiency

Abstract

This study focused on partial data offloading with mobile edge computing over a fifth-generation mobile network, using optimisation techniques to address joint latency and energy efficiency challenges, focusing on Internet of Things devices. Computational offloading was explored in this study to understand the factors that enable Internet of Things devices to offload complex computational tasks using edge computing. More research on computational offloading for fast data transfer to and from the cloud, specifically in the fifth-generation/telecommunications context, needs to be done. Limited research focuses on joint latency and energy efficiency optimisation to offload data to the cloud versus process data at the Internet of Things device. A literature review discovered various aspects of studying mobile edge computing and computational offloading was provided. A review of the literature identified several aspects of researching mobile edge computing and computational offloading. The edge computing technique used is mobile edge computing. A multi-objective optimisation problem incorporating computational offloading via latency and energy usage is utilised to solve the situation using the Lagrange function. The model is structured around minimising the energy for uplink transmission, local computation, and downlink transmission, which are the significant factors in energy and latency. The simulations are simulated on MATLAB and compared to existing research models, and conclusions are provided. This model encompassed the data transmitted as a function of the equation as a multi-objective optimisation problem focusing on latency and energy efficiency utilising overall energy usage, latency in edge execution, iii transmission/upload lag, queuing latency, download latency, and local processing duration. The solution is analysed and compared against leading papers in the field. The model demonstrates the efficient means of transmitting partial data from mobile edge computing to the cloud in the fifth generation of mobile networks using minimal latency and the highest energy efficiency, which yield similar results to papers compared whilst increasing accuracy