RESOURCE SCHEDULING AND COMPUTING OFFLOADING STRATEGY FOR INTERNET OF THINGS IN MOBILE EDGE COMPUTING ENVIRONMENT

In order to solve the problem that the computing resources and battery energy of existing mobile devices cannot meet the rapid development of computing-intensive and delay-sensitive applications, this paper proposes a resource scheduling and computing offloading strategy for Internet of Things (IoT) in a mobile edge computing environment. Firstly, the paper designs a computing offloading and resource allocation system model. The model includes Macrocell Base Station (MBS) with Mobile Edge Computing (MEC) servers deployed and Smallcell Base Station (SBS) that can be used as a relay to achieve flexible resource management and control. Then, the computing model and communication model are built according to system model. Besides, the optimization problem is established considering minimizing the system delay and energy consumption as optimization goal. Finally, introducing game theory, the potential game model is used to solve resource allocation and computing offloading problem. Mobile devices can select MEC nodes for computing offloading according to the game result. The simulation experiment of our proposed strategy is carried out based on MATLAB platform. Experimental results show that the proposed potential game equation can converge to Nash equilibrium. Moreover, when the number of mobile users is 1000, the energy consumption and delay of the proposed strategy are 240 J and 13 ms respectively, which are better than other comparative strategies.