Secure Collaborative Computation Offloading and Resource Allocation in Cache-Assisted Ultra-Dense IoT Networks With Multi-Slope Channels

TL;DR

This paper proposes a secure, energy-efficient computation offloading scheme for cache-assisted ultra-dense IoT networks, integrating OFDMA, NOMA, and security measures, optimized via a hierarchical adaptive search algorithm.

Contribution

It introduces a novel secure offloading and resource allocation framework combining multiple access schemes and a new FIHAS algorithm for ultra-dense IoT networks.

Findings

Achieves lower energy consumption and delay compared to existing algorithms.

Effectively balances security, latency, and cost constraints.

Demonstrates the efficiency of the proposed algorithm through simulations.

Abstract

Cache-assisted ultra-dense 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 paper explores the combination of orthogonal frequency division multiple access (OFDMA), non-orthogonal 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. As for such a 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, giving 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.