Ultra-dense networks are widely regarded as a promising solution to explosively growing applications of Internet-of-Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are utilized at first. Then, in order to attain a goal of green and secure computation offloading, under the proportional allocation of computational resources and the constraints of latency and security cost, joint device association, channel selection, security service assignment, power control and computation offloading are done for minimizing the overall energy consumed by all IMDs. It is noteworthy that multi-step computation offloading is concentrated to balance the network loads and utilize computing resources fully. Since the finally formulated problem is in a nonlinear mixed-integer form, it may be very difficult to find its closed-form solution. To solve it, an improved whale optimization algorithm (IWOA) is designed. As for this algorithm, the convergence, computational complexity and parallel implementation are analyzed in detail. Simulation results show that the designed algorithm may achieve lower energy consumption than other existing algorithms under the constraints of latency and security cost.

翻译：超密集网络被广泛认为是应对物联网移动设备（IMDs）爆炸式增长应用需求的一种有前景解决方案。然而，该类网络中复杂且严重的干扰问题亟待妥善解决。为此，本文首先同时利用正交多址接入（OMA）与非正交多址接入（NOMA）技术。继而，为实现绿色且安全的计算卸载目标，在计算资源比例分配以及时延与安全成本约束条件下，联合优化设备关联、信道选择、安全服务分配、功率控制与计算卸载，以最小化所有IMD的总能耗。值得注意的是，本文重点关注多步计算卸载，旨在均衡网络负载并充分利用计算资源。由于最终构建的问题属非线性混合整数形式，其闭式解难以求得。为此，设计了一种改进鲸鱼优化算法（IWOA）。本文详细分析了该算法的收敛性、计算复杂度及并行实现能力。仿真结果表明，在时延与安全成本约束下，所提算法能够比现有其他算法实现更低的能耗。