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. 2017 May 4;17(5):1022.
doi: 10.3390/s17051022.

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

Ning Li  1 , Baran Cürüklü  2 , Joaquim Bastos  3 , Victor Sucasas  4 , Jose Antonio Sanchez Fernandez  5 , Jonathan Rodriguez  6
Affiliations

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

Ning Li et al. Sensors (Basel). .

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.

Keywords: AUV; probabilistic; topology control; transmission power adjustment; underwater network.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The architecture of the communication network in SWARMs project.
Figure 2
Figure 2
Use case of detection, inspection and traction of plumes in the SWARMs project: (a) AUVs tracking and detecting the plume; (b) AUVs sharing the information between each other.
Figure 3
Figure 3
Average temperature in a year.
Figure 4
Figure 4
Average salinity in a year.
Figure 5
Figure 5
Average temperature with different water depths.
Figure 6
Figure 6
Average salinity with different water depths.
Figure 7
Figure 7
Average sound speed with different water depths.
Figure 8
Figure 8
The network model of PTC algorithm.
Figure 9
Figure 9
The membership functions of input (a) and output (b).
Figure 10
Figure 10
The fuzzy-logic topology control (FTC) scheme.
Figure 11
Figure 11
The average transmission power adjustment probability of PTC-FTC.
Figure 12
Figure 12
The average transmission power adjustment ratio of PTC-FTC and FTC.
Figure 13
Figure 13
The average residual energy of PTC-FTC and FTC.
Figure 14
Figure 14
The average node degree of PTC-FTC and FTC.
Figure 15
Figure 15
The average queue length of PTC-FTC and FTC.
See this image and copyright information in PMC

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