Adaptive delay-constrained resource allocation in mobile edge computing for Internet of Things communications networks

The traffic burden at each node in Internet-of-Things (IoT) communication networks becomes prohibitively high especially when involving exhaustive computation. Mobile edge computing (MEC) makes this complicated computation feasible while alleviates the traffic burden by providing the corresponding node with powerful computing resources through wireless transmission between the node and the MEC for offloading computation. However, the transmission via the varying wireless channel requires considerable energy consumption and imposes delay. In this paper, we study the trade-off between the energy consumption and the delay performance in IoT network due to the offloading computation and the wireless communication. An optimization problem involving the offloading ratio for MEC as unknown parameter is established by minimizing the total energy consumption subject to a delay constraint. The problem is then solved by analyzing the convexity of the cost function and the constraint. Moreover, the scaling law of both energy cost and delay performance of IoT networks is investigated with respect to the number of nodes employing the MEC. It is discovered that the delay performance decreases in the logarithm with increasing the number of nodes while the energy cost grows linearly with the increase of the number of nodes. Numerical simulations verifying the performances of the proposed method in the studied IoT networks with MEC are provided.