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. Recently, a novel concept i-e., Digital Twin (DT) has re ceived considerable attention in academia and industry due to its ability to monitor,
analyze, and optimize physical systems. It connects the physical systems with the
virtual spaces. Moreover, it helps to optimize decision-making. Therefore, this thesis
aims to accommodate the maximum IoT nodes and increase the network through put simultaneously by considering the IoT node association and power allocation in
the Digital Twin-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. Generally, the DT technology helps in making more accurate and opti mized 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 (MINLP). To solve the proposed problem, the Outer
Approximation Algorithm (OAA) is used due to its lesser complexity. The proposed
algorithm’s convergence, effectiveness, and lesser complexity leads to ξ-optimal solu tion = 10−3
, achieved using standard problem solvers. The simulation results in terms
of associated IoT nodes and the network’s throughput demonstrate the proposed ap proach’s effectiveness. The results show that the network throughput and associated
IoT nodes are increasing with an increase in considered IoT nodes. Also, when the re viii
sults of the DT-assisted MEC network are compared with the MEC network, it shows
that by using the proposed approach, the DT-assisted MEC network has greater net work throughput than the MEC network. Moreover, the proposed DT-assisted MEC
network can facilitate more IoT nodes as compared to the MEC network.
