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Sensors and Materials, Vol. 31, No. 3 (2019) 845–858
MYU Tokyo
S & M 1819

Underwater Sensor Network Deployment Algorithm
Using Density-based Spatial Clustering of Applications with Noise
Hui Wang,* Tingcheng Chang, Yexian Fan, and Zhiliang Li
Department of Computer Science Engineering, Ningde Normal University,
No. 1 Xueyuan Road, Ningde 352000, China(Received September 30, 2018; accepted January 23, 2019)

Keywords: underwater sensor networks, DBSCAN, single or multiple hops, global search

Underwater sensor networks have received extensive attention owing to their promise forapplication to marine exploration, submarine navigation, and pollution monitoring. In an open
ocean underwater environment, the targets to be monitored are undefined. Therefore, it isnecessary to investigate how sensor nodes adjust their positions autonomously in accordancewith the changes in the environment and targets to achieve optimal monitoring quality. We
propose a fish-swarm-inspired underwater sensor network deployment algorithm using densitybased spatial clustering of applications with noise (DBSCAN). Firstly, inspired by the operation
mode of an artificial fish-swarm system, sensor nodes autonomously cover all the events bysimulating physiological behaviors such as swarm, follow, and prey. Secondly, considering thecomplexity of the underwater environment and also to reduce the number of nodes participatingin the movement and to avoid the blind movement of nodes, the DBSCAN model is introduced
to achieve the sharing of sensed information among the nodes that communicate by the singlehop or multihop mode, thereby enhancing the global search ability of nodes. Finally, a large
number of experiments are carried out to evaluate the performance of the proposed algorithm.
The results show that the proposed algorithm can effectively solve the problem of underwater
sensor deployment, and has the advantages of fast convergence and strong scalability.

1.

Introduction

Underwater sensor networks are widely used for marine tactical reconnaissance, early
warning of disasters, marine exploration, and submarine navigation, and therefore are attracting
increasing attention.(1,2) By mounting sensors on an autonomous underwater vehicle orunderwater mobile device, an underwater mobile sensor node is formulated to perform a task
underwater. A reasonable and effective sensor node deployment can significantly reduce thetime taken to construct a sensor network and quickly cover the target area. It can also prolong
the network life and adapt to the changing topology through coordinated control.
In an underwater sensor deployment scheme, nodes are deployed in an underwater threedimensional space to achieve a stereoscopic perception of the monitored area. The existing*

Corresponding author: e-mail: wangh0802@163.com
https://doi.org/10.18494/SAM.2019.2159
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 31, No. 3 (2019)

three-dimensional underwater sensor deployment schemes can be divided into two types. The
first type is for uniform coverage, that is, sensor nodes are uniformly deployed in the monitoredarea.(3–5) The second type is for nonuniform coverage, that is, nodes are nonuniformly deployedin accordance with the specific distribution of the monitored targets.(6–10) In the nonuniform
coverage scheme, the concept of “event” is explicitly proposed. Thus, the purpose of node
deployment is no longer to uniformly cover the monitored area, but to cover the events. Suchnode deployment is more practical and effective, and also conforms to the characteristics of thesparseness of underwater sensor networks. In this paper, we focus on sensor node deployment
for nonuniform coverage in underwater three-dimensional space and the design of a distributed
underwater sensor network node deployment algorithm based on an artificial fish swarm.
Because of the characteristics of adaptive self-deployment and a distributed operation mode, theproposed algorithm is suitable for complex and harsh environments, such as mountains, oceans,and deep sea trenches. At the same time, owing to the limited energy of nodes and to avoid theblind movement of nodes, we propose a density-based spatial clustering of applications withnoise (DBSCAN) algorithm to cluster the nodes. Satisfactory deployment results are achievedby combining the DBSCAN algorithm and the intelligent optimization algorithm of an artificial
fish swarm.
The rest of this paper is organized as follows. In Sect. 2, we describe some related work.
Problems are described and a model is established in Sect. 3 to help understand the algorithm
presented in this paper. A solution for the problem is proposed in Sect. 4. In Sect. 5, thesimulation experiments and analysis of results are presented. Finally, conclusions are given in
Sect. 6.

2.

Related WorkFor node deployment with nonuniform coverages, many achievements have been made athome and abroad. Wang et al.(11) proposed an optimized artificial fish swarm algorithm (OAFSA),
where the maximum coverage rate is obtained by simulating the prey and follow behaviors in
artificial fish. The optimized OAFSA effectively improved the coverage performance comparedwith the traditional artificial fish algorithms, but the deployment in complex environments(such as those with obstacles) was not considered in this research. Lian et al.(12) introduced
four weighting indices (i.e., effective coverage rate, high monitoring probability range ratio,
spatial overlap ratio, and resource utilization) to adjust the fitness function to improve theeffective coverage rate of the sensor network. However, the algorithm had slow convergence,
and as the population size increased, the computational complexity of the algorithm increased
exponentially. Aitsaadi et al.(13) considered the difference in the distribution density of events inthe monitored waters and proposed an algorithm for nonuniform node deployment by the meshrepresentation method. This algorithm could help the network achieve effective monitoring forevents, but it was difficult to obtain a higher network connectivity rate. To effectively cover
the events in the monitored waters, Du et al.(14) proposed particle-swarm-inspired underwater
sensor self-deployment (PSSD), in which particle swarm optimization and congestion degree
control are combined to effectively solve the network coverage problem. The complexity wasSensors and Materials, Vol. 31, No. 3 (2019)847

low, the convergence speed was high, and an effective node distribution could be realized.
However, this PSSD algorithm only considered the coverage of the network for events, and it
was also difficult to obtain a high network connectivity rate. In addition, this kind of algorithm
has some disadvantages, for example, too many nodes participate in the movement and nodes
may move blindly. Because of the limited energy of nodes and high energy consumption in anunderwater environment, the nodes will fail owing to the rapid consumption of energy, which
shortens the life of the sensor network.
The above methods have achieved good results in different aspects of underwater sensor
node deployment, but cannot completely meet the needs of underwater sensor network
applications. The main problems are as follows. Most of the methods are centralized
optimization methods, making them difficult to implement with distributed nodes; the nodes are
deployed for certain events, and it is difficult to adaptively adjust node deployment to ensure asatisfactory monitoring quality for uncertain events in an open-ocean underwater environment
or for events that dynamically change depending on external factors such as ocean currents.
For the above problems, inspired by the operation mode of the fish swarm system, a distributed
and achievable underwater sensor node deployment algorithm is proposed in this paper. By
simulating fish swarm behaviors such as prey, the sensor nodes autonomously cover events.
The combination of the proposed algorithm and the DBSCAN algorithm greatly improves theconvergence speed, avoids the blind movement of nodes, and prolongs the life of the underwater
sensor networks.

3.

Background3.1

Problem description

Assuming that N sensor nodes are deployed in the monitored area A and si represents the ith
node in the network, then the set of underwater sensor nodes is S = {s1, s2, ..., sN}. Assumingthat a node, si, has the ability to sense, communicate, and move, then Bi = ris , ric , li , whereris ≥ 0 is the sensing radius of si, r c ≥ 0 is the communication radius of si, and li ≥ 0 is theimaximum moving step length of the node. It can be seen that, in a homogeneous network, allnodes have the same attributes, namely, ris = r s, ric = r c, and li ≥ l (1 ≤ i ≤ N). The dynamic
point of interest e is referred to as an event, and the sensor node can detect the event andcommunicate with the neighbor node to obtain state information (the number of covered events)of the neighbor node. The task of a node is to cover the event and collect information about theevent.
3.2

Coverage probability model

It is assumed that the monitored area A is a three-dimensional cuboid space (m × n × p),
and that the coverage model of each node is a sphere with a sensing radius of ris ≥ 0. The
communication range is a sphere with a radius of ric ≥ 0. To ensure network connectivity, thecommunication radius is set to be greater than or equal to twice the sensing radius,(15) i.e.,848

Sensors and Materials, Vol. 31, No. 3 (2019)ric ≥ ris. In the monitored area A, for ∀ei ∈ A, if the Euclidean distance between the node sj and()

ei i,,ssjj) ≤ r js, then the event sj is covered
the event ei represented by d(ei, sj) satisfies ≤ r js, that is,dd(eby the node sj. d(ei, sj) is given by() ( xi − x j ) + ( yi − y j ) + ( zi − z j ) ,2

d ei , s j =

2

2

(1)

where (xi, yi, zi) is the coordinate of the event ei and (xj, yj, zj) is the coordinate of the node sj. In
the monitored area A, for ∀ei ∈ A, the probability of the node sj covering the event ei is givenby(16)()

1,d ei , s j ≤ r − λ  d ( ei , s j )− r  p ei , s j, r < d ei , s j ≤ r s ,=e
d ei , s j > r s
0,


(

)

(

(

)

)

(2)


where r is the radius of the self-confidence circle of the node; λ is the attenuation factor ofsensing and is the physical property of the node. Then, in the monitored area A, the number of
events covered by the node sj is expressed as

( )

(

)

N e s j = ∑ p ei , s j .
ei ∈A

(3)

When the event ei is not within the sensing range of any node in the node set S, the event ei
can be considered to be uncovered for the entire node set S. Therefore, by using multiple sensor
nodes to simultaneously detect the event ei, the joint coverage probability of points is given byN((

P ( ei , S ) =1 − ∏ 1 − p ei , s j
i =1

)) .

(4)

4.

Design of Fish-swarm-inspired Underwater Sensor Deployment Algorithm
Based on DBSCAN

4.1

Basic fish swarm algorithm

An artificial fish swarm algorithm (AFSA) is an optimization algorithm that simulates the
behavior of a fish swarm. Using the behaviors of a fish swarm, such as prey, cluster, and follow,
the global optimal solution can be found rapidly. The AFSA is a kind of swarm intelligencestochastic optimization algorithm. Similarly to the particle swarm and ant colony algorithms,
the AFSA also has a strong global search ability and is insensitive to initial value parameterselection, robust, and easy to operate.

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Assuming that, in an n-dimension search space, there are N artificial fish that make up aswarm. The state of each artificial fish can be expressed as X = (X1, X2, ..., X N), where Xi (i =1, 2, ..., N) is the variable to be optimized. The food concentration for the artificial fish at thecurrent position is expressed as Y = f(X), where Y is the objective function; the distance between
two artificial fish is expressed as d=ij

X i − X j ; Visual represents the sensing range of one

artificial fish; Step is the step length of artificial fish movement; δ is the congestion degree
factor; tryNum represents the maximum number of trials of an artificial fish for each prey.
The artificial fish uses its vision to sense the external environment, as shown in Fig. 1. Xi isthe current position of the artificial fish; Visual (equivalent to the communication radius) is thevisual distance; X h is the visual position at a certain moment. If a position within the field ofview is better than the current position, the artificFial fish will go further in this direction to thenext position Xnext; otherwise, it continues to wander within the field of view.
The process of a mobile node in a sensor network aimed at greater network coverage issimilar to the follow and prey behaviors of artificial fish. The food concentration at the locationof an artificial fish can be regarded as the network coverage under the current state. Usually,
fish will stay in a place rich in food, so the global optimal behavior can be found by simulating
the characteristics of fish, which is the basic idea of the AFSA. The basic behaviors of artificial
fish are as follows(1) Prey behavior: Fish swim randomly to search for food. Assuming that Xi is the current stateof an artificial fish, a state Xj is randomly selected within its visual range and Y is the foodconcentration (objective function). The larger the value of Visual, the easier it is to find theconvergent global extremum.
Xj =X i + Visual × Rand ( )(5)

If Yi < Yj, the artificial fish will go further in this direction; otherwise, it randomly selects astate Xj and determines whether the condition is met. If the condition is still not satisfied after
repeating it tryNum times, it moves one step at random. When the value of tryNum is small, theartificial fish can swim randomly so that it can escape the local extremum.

Fig. 1.

Schematic diagram of the visual range of artificial fish.

850

Sensors and Materials, Vol. 31, No. 3 (2019)( t +1)(t )Xi=X i + Visual × Rand ( )(6)(2) Cluster behavior: Fish naturally gather together during movement. Assume that Xi isthe current state of artificial fish, Xc is the location of the swarm center, nf is the number ofcompanions in the current neighborhood (dij < Visual), and N is the total number of fish. The
conditions Yc > Yi and nf /N < δ indicate that the swarm center has more food (high fitness
function value) and is not very congested, then fish Xi will go further toward the center.
( t +1)

Xi

(t )

= Xi

(t )
Xc − Xi+× Step × Rand ( )(t )Xc − Xi(7)

Otherwise, prey behavior is performed.
(3) Follow behavior: When one fish finds that food is abundant in the current place, other fish
will quickly follow. Assuming that Xi is the current state of the artificial fish, it finds that acompanion Xj is nearby (dij < Visual) where Yj is large. If Yj > Yi and nf /N < δ, it means thatcompanion Xj has a higher food concentration (high fitness function value) and the surrounding
environment is not very congested, so fish Xi will go further toward companion Xj.
( t +1)

Xi

(t )

= Xi

(t )
X j − Xi+× Step × Rand ( )(t )X j − Xi

(8)

Otherwise, prey behavior is performed.
It should be noted that the application of the cluster and follow behaviors of a fish swarm canmake the node move closer to the fish with high food concentration. At the same time, in orderto maximize the coverage rate, the fish swarm should maintain good formation, which can becontrolled by the congestion degree factor δ and fish spacing.
4.2

Description of DBSCAN algorithm

The DBSCAN clustering algorithm can find clusters of arbitrary size and arbitrary shape,and effectively identify outliers,(17) and is insensitive to the order of input objects. Because of
these advantages, the DBSCAN algorithm is widely used in many fields. For a d-dimensional
data set Ω (i = 1, 2, ..., d), the definition in DBSCAN is as follows.(18)Definition 1 Eps neighborhood of data point ρ: a circular area with radius Eps andcentered on point ρ in the data set. The set of points contained in the area is denoted as
N E ps ( ρ =){q ∈ Ω d ( q, ρ ) ≤ E }.
psDefinition 2 Density of data point ρ: the number of data points in the Eps neighborhood ofdata point ρ in data set Ω.
Definition 3 Core point: If the density of data point ρ is greater than or equal to MinPts, it iscalled the core point, where MinPts is a threshold specified by the user.

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Definition 4 Boundary point: Data point q is not the core point, but it is in the Epsneighborhood of another core point ρ; thus, it is called a boundary point.
Definition 5 Noise point: Other points in the data set that are neither core points nor
boundary points are called noise points.
Definition 6 Directly density-reachable: Let data point q be the core point. As long as
data point ρ belongs to the Eps neighborhood of data point q, data point ρ is said to be directly
density-reachable from data point q.

ρ ∈ N E ps ( q )(9)N E ps ( q ) ≥ MinPts(10)

Definition 7 Density-reachable: If there is a data point chain ρ1, ρ2, ..., ρn, where ρ1 = ρ, ρn = q,ρρi ∈ Ω
Ω, 1 ≤ i ≤ n, and it is direct arrived density from ρi to ρi+1, then data point ρ is said to bedensity-reachable from data point q.
Definition 8 Density-connectable: In data set ρ, for a given Eps and MinPts, if there is a pointo capable of making data points ρ and q density-reachable from data point o, data point ρ anddata point q are said to be density-connectable.
To facilitate the understanding of the above concepts, Fig. 2 shows the schematic diagram of
the DBSCAN algorithm when MinPts = 4. The D point and other red points in the figure are thecore points, because the area around these points within the Eps radius contains at least 4 points(including the point itself). Because they are mutually reachable, they form a cluster. Point B
and point C are not core points but can be reachable from point D (through other core points)
and therefore belong to the cluster. Point G is neither a core point nor a directly reachable noise
point.
For the given Eps and MinPts, a summary of the flow of the DBSCAN algorithm is asfollows. Select any undivided data object to determine whether it is a core data object, andif so, find all data objects that are density-reachable from it and mark these data objects as a

Fig. 2.

(Color online) Schematic diagram of the DBSCAN algorithm when MinPts = 4.

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Sensors and Materials, Vol. 31, No. 3 (2019)cluster; if not, the noise judgment is performed on the data. If it is a noise point, it is marked. If
not, the object is not processed. The above steps are repeated until all data objects are divided.
According to repeated experiments, the parameter Eps is equal to rc in this research, and its
role is to cluster the nodes that can realize data transmission by single or multiple hops in theunderwater sensor networks. At the same time, MinPts is set to 2, indicating that the cluster
should contain at least 2 nodes. Figure 3 shows the clustering results of 30 randomly deployed
nodes when Eps = 50 and MinPts = 2. As can be seen from the figure, 30 nodes are clusteredinto 6 categories, where the black nodes are noise points, i.e., isolated states. In addition, from
Cluster #1 to Cluster #5, the data transmission between the nodes in each cluster can be realized
in the single-hop or multihop mode.
4.3

Proposed algorithm

In underwater node deployment, a sensor node is equivalent to the artificial fish in AFSA
inspired by the operation mode of an artificial fish swarm, and an event is equivalent to food.
The process of sensor nodes going forward to events is equivalent to the process of artificial fishsearching for food. However, considering that the single use of the fish swarming algorithm
will cause too many nodes to participate in the movement and that the nodes are moving blindly,
the nodes will fail rapidly owing to the rapid exhaustion of energy, which will shorten the life of
the network. Therefore, faced with the complexity of an underwater environment, we propose
a fish-swarm-inspired underwater sensor deployment algorithm based on DBSCAN, where thenodes are clustered in the process of artificial fish updating their states to realize the sharing ofsensed information within the fish swarm. Therefore, the artificial fish are guided to migrate toa better state, thus speeding up the convergence of the optimized deployment of artificial fish.
Definition 9 Congestion degree factor: In the monitored area A, the allowed congestion
degree at node si (1 ≤ i ≤ n) is defined asσ ( si )= ψ × N e ( si ) ,Fig. 3.

(Color online) Clustering results of 30 randomly deployed nodes when Eps = 50 and MinPts = 2.

(11)

Sensors and Materials, Vol. 31, No. 3 (2019)853

where ψ is a constant indicating the expected coverage of a single event and Ne(si) indicates thenumber of events covered by node si.
Initialization: n nodes are randomly scattered in the underwater monitored area A. The
DBSCAN clustering algorithm is performed as described in Sect 4.2. Then, node si (1 ≤ i ≤ n)belongs to cluster Cz(si), where card(Cz(si)) indicates that cluster Cz(si) contains n nodes. Next,
node si performs the following actions in accordance with its own state and those of neighbornodes.
(1) Prey behavior: If card(Cz(si)) = 0, node si is in an isolated state, and prey behavior is
performed. Assuming that the current position of the node is Xi, and within its maximum
moving step length l, it randomly moves to a new position Xnext.
X next =X i + rand ( l ) ⋅ T(12)rand(l) is a random number between 0 and l, and T is an arbitrary unit vector. If the number of
events covered by node si increases, prey behavior is successful; otherwise, a new location isagain randomly selected.
(2) Follow behavior: If card(Cz(si)) > 1, the optimal node sopt is selected in the cluster Cz(si).
s(sopt) < σ(sopt),
If node sopt covers more events and is less congested, i.e., Ne(sopt) ≥ Ne(si) and N cothen node si goes further in the direction to the position of sopt, i.e.,

X next= X i + l′ ×

X opt − X i
X opt − X i

,

(13)

where Xi and Xopt represent the position vectors of si and sopt, respectively. l′ is the moving step
length expressed as1
1
d si , sopt < l d si , sopt ,l′ =  22
l.
otherwise

(

)

(

)

(14)

If Ne(si) increases, the follow is successful; otherwise, it fails.
(3) Cluster behavior: If card(Cz(si)) > 2, cluster behavior is performed. Firstly, determine thecenter locations sc of the nodes in cluster Cz(si).
Xc =

1
∑ Xk
card ( C z ( si ) ) sk ∈Cz ( si )(15)

At the same time, the average number of covered events of sc is given by
N e ( sc ) =1∑ Ne ( sk ) ,card ( C z ( si ) ) sk ∈Cz ( si )

(16)

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and the number of neighbor nodes is given bys
N co( sc ) =1sN co( sk ) .
∑card ( C z ( si ) ) sk ∈Cz ( si )(17)

If there are more covered events and less congestion at sc, i.e., Ne (sc) ≥ Ne (si) ands(s( sc)c )<<σ(sN coσ c(),scthen) the node si goes further in the direction toward the position of sc, i.e.,
X next = X i + l ′ ×

Xc − Xi,
Xc − Xi(18)

where Xi and Xopt represent the position vectors of si and sopt, respectively. l′ is the moving step
length expressed as1
1
d si , sopt < l d si , sopt ,l′ =  22
l.
otherwise

(

)

(

)

(19)

If Ne(si) increases, the cluster behavior is successful; otherwise, it fails.
On the basis of the above discussion, a complete fish-swarm-inspired underwater sensor
deployment algorithm based on DBSCAN is presented, as shown in Algorithm 1.
Algorithm 1: Fish-swarm-inspired underwater sensor deployment algorithm based on DBSCAN
Initialize the maximum number of iterations Nmax and the number of trials in the prey1:behavior Ntry;
Initialize the nodes S = s1, s2, ..., sn;2:while not (Nmax) do
3:
for all sii ∈ S do
4:
		 Obtain the node cluster: Ci ← dbscan(S, ε, λ);5:		 The number of events covered by node si: Ne(si)6:		if Ne(si) > 0 then7:			 Move to the center of the covered event8:
		end if
9:
10: 		if card(Ci(si)) > 2 then11: 			 Find optimal neighbor node sopt in cluster Cz(si) where node si is located;
12: 			 Find the center positions of all nodes in cluster Cz(si) where node si is located;s(s
13: 			if Ne(sopt) ≥ Ne(si) and N co
( soptc ) <<σσ(s( soptc )) then
14: 				Node si moves to node sopt and the follow behavior is performed;s(s
σ c()scthen
15: 			else if Ne(sc) ≥ Ne(si) and N co
( sc)c )<<σ(s)
16: 				Node si moves to node sc and the cluster behavior is performed;
17: 		else
18: 			for k ← 1 to Ntry doSensors and Materials, Vol. 31, No. 3 (2019)

855

19:
20:
21:
22:
23:
24:
25:
26:
27:
28:
29:

				Prey behavior is performed;
				ifNNee(( si′)) > Nee(s( is)i then) break;				end if
			end for
else
		for k ← 1 to Ntry do			 Prey behavior is performed;
			ifNNee( si′)) > Nee(s( is)i then) break;			end if		end forend while

5.

Simulation Experiment and Analysis

The PSSD algorithm is a typical nonuniform deployment algorithm for an underwater
wireless sensor node network. To evaluate the performance of the proposed algorithm, the
PSSD algorithm was used for comparison, in terms of network coverage rate, total moving
distance of nodes, and running time. To eliminate the random effects of the experiment, 30
experiments were conducted and their averaged data were used. The parameter settings of the
proposed algorithm are shown in Table 1.
The three-dimensional monitored area was 200 × 200 × 200 m3; 40 target events wererandomly distributed in the water, and the size and position were randomly configured; six
sensor nodes were randomly deployed. The self-organized deployment of sensor nodes wasachieved by the algorithm proposed in this paper. The results of running the algorithm areshown in Fig. 4, where (a) shows the initial state of node deployment and (b) shows the resultof running the proposed algorithm. The blue stars indicate the event distribution, the red
dots indicate node positions, and the sphere indicates the three-dimensional sensing range ofthe node. It can be seen that the proposed algorithm can track the event distribution well andachieve effective coverage of all events.
Figure 5 shows the average coverage rates of the proposed and PSSD algorithms with thenumber of iterations. In the figure, the abscissa represents the number of iterations and theordinate represents the average coverage rate of 30 experiments. It can be seen that, under
the same initial state, the proposed algorithm achieves not only a higher network coverage
rate, but also a full coverage of events after a few iterations. Furthermore, the convergence isfaster. Compared with the PSSD algorithm, the clustering of nodes by DBSCAN enables the
proposed algorithm to overcome the disadvantage of the blindness of the random search by
the traditional fish-swarm-inspired algorithm. Figure 6 shows the total moving distances ofTable 1
Parameter settings.
rs
50 m

rc
ε
100 m 100 m

λ
2

Ntry
5

l
15

ψ
0.1

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Sensors and Materials, Vol. 31, No. 3 (2019)

(a)

(b)

Fig. 4. (Color online) Result display of self-organized deployment. (a) Initial state and (b) result of running the
proposed algorithm.

Fig. 5(Color online) Comparison of averagecoverage rates.

Fig. 6(Color online) Comparison of total moving
distances of nodes.

nodes for the proposed and PSSD algorithms with the number of iterations. It can be seen that,when the number of iterations is the same, the proposed algorithm can greatly reduce the total
moving distance of nodes compared with the PSSD algorithm, thereby saving node energy
and prolonging the network life. The main reason behind this phenomenon is that information
sharing between nodes that can realize data transmission in the single or multihop mode isachieved with the help of the DBSCAN algorithm, thus improving the global sensing abilityof the distributed fish swarm algorithm, avoiding blind movement, and reducing energy loss
during node movement.
Above, the deployment of 40 target events and 6 sensor nodes was analyzed. However,to obtain accurate information, a large number of sensor nodes (far more than 6) are usuallydeployed in the monitored area. Therefore, below, we will consider the effect of the change in
the number of nodes on the performance of the proposed algorithm.

Sensors and Materials, Vol. 31, No. 3 (2019)

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Table 2Average moving distance of nodes for the number of nodes from 6 to 30.

PSSD
Proposed algorithm

675.2975
56.7577

Number of nodes
10
14
22
68.0178 59.5518 47.7318
49.2103 47.3806 25.4953

30
27.9360
13.4761

Table 3
Average running time for the number of nodes from 6 to 30.

PSSD
Proposed algorithm

60.0996
0.1634

Number of nodes
1014
22
0.10830.12790.15340.20910.27880.340830
0.21470.3706

Table 2 shows the average moving distance of nodes for the number of nodes from 6 to
30. It can be seen from Table 2 that, as the number of nodes increases, the average moving
distance of nodes decreases to varying extents, but compared with the PSSD algorithm, theproposed algorithm shows a clearer decreasing trend. This is because as the number of nodes
increases, the number of nodes participating in the sharing of sensed information through thesingle or multihop mode also increases, whereas the PSSD algorithm mainly makes decisions
by sensing the information of nodes within the communication range. In other words, theproposed algorithm is better at sensing global information and then finding the location of thetarget event. As a result, fewer nodes participate in the movement, and the nodes are no longer
moving blindly. Therefore, the total moving distance of the nodes is reduced.
Table 3 shows the average running time for the number of nodes from 6 to 30. It can be seenfrom the table that, as the number of nodes increases, the running times of the two algorithms
are extended to varying extents. Compared with the PSSD algorithm, the proposed algorithm
has a longer running time when the number of nodes is the same. This is mainly because eachnode must analyze the sensing state of the nodes in its cluster before making a decision and theprocessing time will increase as the number of nodes in the cluster increases. Therefore, interms of energy consumption, the energy consumed by node movement is much greater thanthat by data transmission. Moreover, the running time of the proposed algorithm is within theallowable range. Therefore, the effectiveness of the algorithm proposed in this paper is verified.

6.

Conclusions

We proposed a fish-swarm-inspired underwater sensor deployment algorithm based on
DBSCAN. By simulating the three behaviors of a fish swarm (prey, follow, and cluster), nodes
autonomously tend to the area where events are located to achieve the full coverage of all
events. At the same time, to reduce the number of nodes participating in the movement andavoid the blind movement of nodes, DBSCAN clustering analysis is performed to achieve thesharing of sensed information among the nodes that can realize data transmission through thesingle-hop or multihop mode, thereby enhancing the global search ability of nodes.

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Sensors and Materials, Vol. 31, No. 3 (2019)

A large number of experiments were carried out. The results showed that the proposed
algorithm has strong tracking ability and can achieve a high network coverage rate. In
addition, compared with the PSSD algorithm, the proposed algorithm has the following threeadvantages:(1) the node has strong global sensing ability and can track target events rapidly;(2)the disadvantages of too many nodes participating in the movement and the blind movementof nodes are avoided, which effectively solves the energy consumption problem caused by theexcessive movement of the nodes;(3) it exhibits distributed deployment and strong scalability.
Further efforts are expected to be made on solving the problem associated with the fully
connected network.

Acknowledgments
This research was supported by Youth Project of Ningde Normal University (2017Q105,
2018Q103), The Education Department of Fujian Province under Project Number JT180596,
Teaching Reform Project of the Ningde Teachers College (JG2018012) and project of the Fujian
Provincial Natural Science Fund (2017I0016, 2017J01775).

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