UAV-assisted Multiple Access Computation Offloading and Resource Allocation in Ocean Monitoring Network
TL;DR
A joint optimization problem is introduced, which involves optimizing the USN’s transmission power, USV’s transmission power, USV’s transmission power, USV’s offloading decision, and USV’s bandwidth allocation, to meet the requirements of USVs.
Abstract
Given the swift evolution of oceanic surveillance systems and the substantial expansion of diverse marine intelligent devices and services, the serious attenuation of the acoustic channels in underwater sensor networks and the scarce computing resources of the ocean surface have led to the inability to meet the requirements of Unmanned Surface vehicle (USV) users for low latency and low energy consumption. This paper constructs an unmanned aerial vehicle (UAV)-assisted multi-access computing offloading model in ocean monitoring networks. Specifically, in the data collection stage, Underwater sensor nodes (USNs) transmit the collected data to the USVs via non-orthogonal multiple access (NOMA). In the data processing stage, USVs offloads part of the data to the UAV via frequency division multiple access (FDMA), and the UAV and USVs cooperate to complete the data processing task. In order to meet the requirements of USVs, this paper introduces a joint optimization problem, which involves optimizing the USN’s transmission power, USV’s transmission power, USV’s offloading decision, and USV’s bandwidth allocation. Considering the independence between the two stages, the optimization problem is decomposed into two separate stage-problems. The optimization problem of the data collection stage enables collected data to arrive at the USV simultaneously by using the particle swarm optimization algorithm to control the USN’s transmission power. The optimization problem of the data processing stage is decomposed into three sub-problems: the USV’s transmission power, USV’s offloading decision, and USV’s bandwidth allocation, which are optimized separately by using gradient descent, genetic algorithm, and CVX solver respectively. Through iterative alternation, the optimal solution of the data processing stage can be obtained. The simulation results demonstrate a considerable decrease in sum of latency and energy consumption for USVs with the proposed algorithm.