# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Works

- (1)
- the underwater deployment scenarios consider real obstacle factors;
- (2)
- in order to achieve a better effect, when faced with obstacles, the nodes can avoid obstacles;
- (3)
- the obstacle avoidance mechanism avoids node failure due to external force. From a certain extent, it reduces the network energy consumption and guarantees the stability of network topology.

## 3. System Model

#### 3.1. Network Model

_{i}is used to denote the i-th node position, and the corresponding node set is $S=\{{s}_{1},{s}_{2},\dots ,{s}_{n}\}$. The following assumptions are established.

- (1)
- In addition to the sink nodes, all nodes in the network have the same communication and sensing radii.
- (2)
- When the network is initialized, the nodes are distributed randomly in the 3D monitoring space, and the nodes can sense their own location information and their neighbors.
- (3)
- The obstacles (irregular and polyhedral) are distributed randomly in the underwater 3D monitoring space. If a node hits an underwater obstacle in the process of moving, node communication will break down.

#### 3.2. Node Perception Model

_{A}is the number of nodes in the S1 region, and N

_{t}represents the minimum number of nodes to cover the entire partition. N

_{t}depends on the sensing and communication radii of the node. It can be calculated according to the formula as follows:

_{m}represents the volume of a single partition of the node, and V

_{z}represents the volume of the overlapping area. In the same manner, the density of the obstacle is defined as follows:

_{o}is the obstacle density and O

_{m}is the volume of the obstacles detected by the node.

#### 3.3. Underwater Energy Consumption Model

_{tx}(d) expresses the energy that nodes send data as follows:

_{p}is the data transmission time, and P

_{r}are the minimum power packets that can be received. When the transmission distance of A(d) is d, the underwater acoustic signal attenuation model A(d)is provided by

^{a}

^{(f)/10}, as determined by absorption coefficient a(f) [29]. The formulation is as follows:

## 4. RAWS Algorithm

#### 4.1. RAWS Basic Principle

#### 4.2. RAWS Algorithm Description and Process

_{event}(s

_{i}) is the target monitoring points of node B. The formulation is as follows:

_{i}), and the number of neighbor nodes is N

_{nei}(s

_{i}):

_{i}, and the nodes are not too crowded (node concentration D is reasonable). That is, when N

_{event}(s

_{i}) ≠ 0, the node covers the target monitoring point and does not move.

_{i}, that is, N

_{event}(s

_{i}) = 0, two situations exist.

_{event}(s

_{i}) = 0,the nodes will be randomly moved to the new location of P(i)

_{1}in any direction to maintain the diversity of the objective function value in the optimization process (shown in Equation (14)). Assuming that s

_{i}perceives the obstacles in the process of moving, the node will avoid the obstacles and jump to P(i)

_{2}with the greater distance than the node sensing radius. This escape mechanism can not only make the node move safely but also prevent the algorithm from falling into a local optimum of the considered objective function solution:

_{1}is the new position after the node has moved, V represents any unit vector, and ∂ is the speed rate. With the increase in distance, the movement speed decreases, as shown in Equation (15). Rs is the node sensing radius. rand()∙L represents the direction of random movement of nodes, as shown in Equation (16):

_{i}, that is, N

_{event}(s

_{i}) ≠ 0, and node s

_{i}will find the neighbor node with the most coverage monitoring points. This neighbor node is denoted as the best neighbor node s

_{p}(Equation (18)). Then, node s

_{i}moves to the optimal neighbor node in one step, as shown in Equation (19). Assuming that the node encounters obstacles in the process of moving, and the node escapes from these obstacles, as shown in Equation (17):

_{p.}

## 5. Simulation and Performance Analysis

#### 5.1. Introduction of an Algorithm for Comparison and an Evaluation Indicator

- (1)
- each node is independent in the process of the search event. Expanding the search range reduces the internal node mobility and communication;
- (2)
- if a node encounters obstacles in the process of moving, it will avoid them in time and jump out of the local optimal situation;
- (3)
- the algorithm is combined with congestion control, which is conducive to node coverage.

#### 5.2. Simulation Results and Analysis

^{3}, and 20 obstacles were randomly distributed in the water. Initially, 100 nodes were randomly distributed in the monitored water area, and 200 target monitoring points were randomly distributed in the wetland water environment. In the Matlab simulation environment, the process of node deployment and optimization were simulated to verify the performance of the RAWS algorithm.

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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Parameter | Value |
---|---|

Sensing radius R (m) | 30 |

Communication radius r (m) | 60 |

Initial energy consumption Eint (J) | 10 |

Carrier frequency f (KHZ) | 25 |

Energy consumption of data reception Pr (mW) | 3 |

Uncertainty factor Re (m) | 15 |

Moving step length s (m) | 0 < s < 30 |

Moving speed $\partial $ (m/s) | 1 |

Relevant measuring equipment parameters α | 0.2 |

Relevant measuring equipment parameters β | 2 |

Obstacle number Obs | 20 |

Common node number Nod | 100 |

Energy diffusion factor $\eta $ (Khz) | 2 |

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**MDPI and ACS Style**

Jiang, P.; Feng, Y.; Wu, F.
Underwater Sensor Network Redeployment Algorithm Based on Wolf Search. *Sensors* **2016**, *16*, 1754.
https://doi.org/10.3390/s16101754

**AMA Style**

Jiang P, Feng Y, Wu F.
Underwater Sensor Network Redeployment Algorithm Based on Wolf Search. *Sensors*. 2016; 16(10):1754.
https://doi.org/10.3390/s16101754

**Chicago/Turabian Style**

Jiang, Peng, Yang Feng, and Feng Wu.
2016. "Underwater Sensor Network Redeployment Algorithm Based on Wolf Search" *Sensors* 16, no. 10: 1754.
https://doi.org/10.3390/s16101754