As an indispensable key technology in 5G Internet of Things (IoT), mobile edge computing (MEC) provides a variety of computing and\nservices at the edge of the network for energy-limited and computation-constrained mobile devices (MDs). In this paper, we use the\nmultiaccess characteristics of 5G heterogeneous networks and queuing theory. By considering the heterogeneity of base stations, we\nestablish the waiting and transmission consumption model when tasks are offloaded. Then, the problem of jointly optimizing the energy\nand delay consumption of MDs is proposed. We adopt an optimization scheme based on task assignment probability; moreover, the\ntask assignment algorithm based on quasi-Newton interior point (TA-QNIP) method is developed to solve the optimization issue.\nCompared with the Newton interior point algorithm, the proposed algorithm accelerates the convergence speed and reduces the\ncomplexity of the algorithm and is closer to the objective function optimal solution. The simulation results demonstrate that the\nproposed method can reduce the total consumption of MDs effectively; furthermore, the performance of the algorithm is proved.
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