Abstract
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.
Highlights
In the near future, the ocean will supply a substantial part of human and industrial needs: the oil and gas industry will move into deeper waters, the renewable energy will be harvested from sea, as well as many other innovative practices will become common
Note that in this paper, the probabilistic topology control (PTC) algorithm is combined with the fuzzy-logic topology control (FTC) algorithm; in practice, the PTC algorithm can be combined with other different topology control algorithms to improve the performance and reduce the transmission power adjustment ratio
We propose a probabilistic topology control (PTC) algorithm for underwater cooperating autonomous underwater vehicles (AUVs) networks which are associated with limited communication capability and high mobility
Summary
The ocean will supply a substantial part of human and industrial needs: the oil and gas industry will move into deeper waters, the renewable energy will be harvested from sea, as well as many other innovative practices will become common. If P P∗ , but the residual energy of an AUV is small, in this case, it is better to not increase the transmission power in order to prolong the lifetime of this AUV Motivated by these facts, in this paper, we propose a new topology control algorithm called the probabilistic topology control (PTC) algorithm for underwater cooperating AUV networks, which is based on the value of an AUV’s residual energy, queue length, current transmission power, and number of neighbors to determine the transmission power adjustment probability of the AUV. We propose the definition of transmission power adjustment probability Based on this definition, we propose the probabilistic topology control (PTC) algorithm for underwater cooperating AUV networks. The rest of this paper is organized as follows: in Section 2, we introduce the related works published in recent years; in Section 3, we first introduce the channel model, the path loss model, and the network model; we propose the calculation of parameter deviations and the transmission power probability of AUVs; based on the conclusions introduced above, we propose the PTC-based FTC algorithm; Section 4 presents the simulation results of the performance of PTC-FTC algorithm and FTC algorithm; Section 5 concludes the work in this paper
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