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A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

机译:水下协作AUV网络的一种概率高效的拓扑控制算法

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The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.
机译:“协作网格中的智能和联网水下机器人”(SWARM)项目的目的是使无人驾驶水下航行器(AUV),远程操纵车辆(ROV)和无人水面航行器(USV)更加易于使用和使用。为了实现不同AUV之间的合作和通信,这些AUV必须能够交换消息,因此对于SWARM,必须有一个有效而可靠的通信网络。为了为任务执行提供有效而可靠的通信网络,重要和必要的问题之一是水下合作的AUV网络的拓扑控制。但是,由于水下AUV协作网络的特定属性,例如AUV的高移动性,较大的传输延迟,低带宽等,主要为地面无线传感器网络设计的传统拓扑控制算法无法直接用于无线网络中。水下环境。而且,这些算法中的节点一旦当前的发射功率不等于最佳发射功率就调整其发射功率,这在水下合作的AUV网络中成本很高。考虑到这些事实,在本文中,我们提出了一种用于水下协作AUV网络的概率拓扑控制(PTC)算法。在PTC中,当AUV的发射功率不等于最佳发射功率时,将根据AUV的参数确定是否需要调整发射功率。每个AUV根据参数偏差确定自己的传输功率调整概率。偏差越大,发送功率调整概率越高,反之亦然。为了评估PTC的性能,我们将PTC算法与模糊逻辑拓扑控制(FTC)算法相结合,并比较了这两种算法的性能。仿真结果表明,PTC在降低传输功率调整率的同时提高了网络性能。

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