NOMA Assisted Multi-MEC Offloading for IoVT Networks

TL;DR

This paper introduces a NOMA-assisted multi-MEC offloading framework for IoVT networks, enhancing uplink throughput and computation capacity to reduce visual processing delays.

Contribution

It proposes a novel joint optimization framework combining NOMA, multi-MEC offloading, and resource allocation for improved IoVT performance.

Findings

Significant delay reduction achieved through joint optimization.

Enhanced uplink throughput via NOMA in IoVT offloading.

Improved computational efficiency with multiple MEC servers.

Abstract

Nowadays, Internet of Video Things (IoVT) grows rapidly in terms of quantity and computation demands. In spite of the higher local computation capability on visual processing compared with conventional Internet of Things devices, IoVT devices need to offload partial visual processing tasks to the mobile edge computing (MEC) server wirelessly due to its larger computation demands. However, visual processing task offloading is limited by uplink throughput and computation capability of the MEC server. To break through these limitations, a novel non-orthogonal multiple access (NOMA) assisted IoVT framework with multiple MEC servers is proposed, where NOMA is exploited to improve uplink throughput and MEC servers are co-located with base stations to provide enough computation capability for offloading. In the proposed framework, the association strategy, uplink visual data transmission assisted by NOMA and division of the visual processing tasks as well as computation resource allocation at the MEC servers are jointly optimized to minimize the total delay of all visual processing tasks, while meeting the delay requirements of all IoVT devices. Simulation results demonstrate that significant performance gains can be achieved by proposed joint optimization with NOMA transmission and multi-MEC offloading in the heterogeneous IoVT network.