Secure Collaborative Computation Offloading and Resource Allocation in Cache-Assisted Ultradense IoT Networks With Multislope Channels

Abstract

Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed.