Energy-Efficient Task Offloading and Resource Allocation for Delay-Constrained Edge-Cloud Computing Networks

Abstract

The project aims to enhance task offloading in mobile edge computing by addressing the challenges posed by limited computational resources, focusing on maximizing the number of served mobile devices (MDs) while minimizing energy consumption. To achieve this, two primary optimization problems are formulated: a quantity-driven problem to increase the number of served MDs and an energy-driven problem to minimize energy consumption, both of which are NP-hard mixed-integer nonlinear programming challenges. As a solution, a binary tree-based task offloading (BTTO) scheme is proposed, utilizing convex optimization to efficiently derive optimal task offloading decisions. The algorithm is implemented in a simulation environment, where results demonstrate its effectiveness in serving more devices with reduced energy consumption compared to existing techniques. Additionally, the project incorporates extensions such as data compression to minimize transmission time and energy use, along with security measures using SHA256 hash codes to ensure data integrity during offloading.