Nowadays, the Internet of Video Things (IoVT) is growing rapidly in terms of quantity and computation demands. In spite of the higher local computation capability on visual processing compared to 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 off-loading. 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.

中文翻译：

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NOMA 辅助的 IoVT 网络多 MEC 卸载


如今，视频物联网（IoVT）在数量和计算需求方面正在快速增长。尽管与传统物联网设备相比，视觉处理的本地计算能力更高，但IoVT设备由于其更大的计算需求，需要将部分视觉处理任务无线卸载到移动边缘计算（MEC）服务器。然而，视觉处理任务卸载受到MEC服务器的上行吞吐量和计算能力的限制。为了突破这些限制，提出了一种新颖的非正交多址（NOMA）辅助的具有多个MEC服务器的IoVT框架，其中利用NOMA来提高上行链路吞吐量，并且MEC服务器与基站共置以提供足够的计算卸载能力。在所提出的框架中，联合优化关联策略、NOMA辅助的上行视觉数据传输、视觉处理任务的划分以及MEC服务器上的计算资源分配，以最小化所有视觉处理任务的总延迟，同时满足所有 IoVT 设备的延迟要求。仿真结果表明，通过异构 IoVT 网络中 NOMA 传输和多 MEC 卸载的联合优化，可以实现显着的性能增益。