Efficient Resource Allocation in Digital Twin-Assisted Mobile Edge Computing Network

Abstract

Considering the rapid growth in user count and increasing demand for higher data rates, we need to optimize network strategies to accommodate more users and increase the network throughput. Therefore, this work aims to accommodate maximum IoT nodes and increase the network's throughput simultaneously by optimizing the IoT node association and power allocation in the digital twin (DT)-assisted mobile edge computing (MEC) network. The DT of each cloudlet is considered here which helps to optimize the power allocation and IoT node association. The DT technology helps make more accurate and optimized decisions in the MEC network by creating real-time digital representations of physical objects. This work formulates the optimization problem as a mixed integer nonlinear programming problem. To solve the proposed problem, the outer approximation algorithm is used due to its lesser complexity. The proposed algorithm's convergence, effectiveness, and lesser complexity leads to $$-optimal solution = $$, achieved using standard problem solvers. The simulation results in terms of associated IoT nodes and the network's throughput demonstrate the effectiveness of the proposed approach.