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Proceedings ArticleDOI

A Tuned Fuzzy Logic Relocation Model in WSNs Using Particle Swarm Optimization

06 Jan 2013-pp 1-5

TL;DR: It is shown that by applying PSO to the linear combinations of desired metric(s) to obtain tuned fuzzy parameters, the relocation model outperforms and/or is comparable to DSSA in one or more performance metrics.
Abstract: In harsh and hostile environments, swift relocation of currently deployed nodes in the absence of centralized paradigm is a challenging issue in WSNs. Reducing the burden of centralized relocation paradigms by the distributed movement models comes at the price of unpleasant oscillations and excessive movements due to nodes' local and limited interactions. If the nodes' careless movements in the distributed relocation models are not properly addressed, their power will be exhausted. Therefore, in order to exert proper amount of virtual radial/angular push/pull forces among the nodes, a fuzzy logic relocation model is proposed and by considering linear combination of the presented performance metric(s)(i.e. coverage, uniformity, and average movement), its parameters are locally and globally tuned by particle swarm optimization(PSO). In order to tune fuzzy parameters locally and globally, PSO benefits respectively from nodes' neighbours within different ranges and all the given deployed area. Performance of locally and globally tuned fuzzy relocation models is compared with one another in addition to the distributed self-spreading algorithm (DSSA). It is shown that by applying PSO to the linear combinations of desired metric(s) to obtain tuned fuzzy parameters, the relocation model outperforms and/or is comparable to DSSA in one or more performance metric(s).

Summary (3 min read)

Introduction

  • Personal use of this material is permitted.

III. METHODS AND ASSUMPTIONS

  • With the given sensing range Rs and transmission range Rc, sensor nodes are modeled as unite disk graphs (UDG) and are bi-directionally connected when they reside within their one another’s ranges.
  • Nodes are randomly deployed in 2D rectangular field of [xmin xmax] × [ymin ymax] with the uniform distribution.
  • Nodes’ locations are known by either centralized or distributed localization algorithms [19], [20].
  • Circular zone around the node is defined as a circle with radius of Rzone (Rzone = k · Rc) with the node in the center of circle and are used to obtain the fuzzy parameters from nodes’ neighbours residing in the given zone via PSO.

A. Fuzzy Logic Parameters

  • Fuzzy rule-based systems are applied in a variety of research areas [21], [22].
  • Similar to [10] two different fuzzy inference systems are used: fuzzy radial pair force and fuzzy angular force.
  • Figure 1 is brought as the example of respectively tuned radial and angular membership functions for angular strategy A1, boundary condition B2 and movement strategy FRAM .
  • Thus, parameters can be tuned based on one or linear combination of the metrics.
  • PSO is applied for each selected node with a zone-range of Rzone (Rzone = k · Rc) around selected node by taking account node’s neighbours residing within its Rzone range.

B. PSO structures

  • The constriction coefficient PSO used similar to the [23].
  • The parameter k in the equation 6 controls the exploration and exploitation.
  • Each particle consists of two arrays, which one is related to the memberships of the pair force fuzzy systems and another one is related to the memberships of the angular force fuzzy systems.
  • Each fuzzy system has 5 memberships and each membership is specified by its mean and variance, therefore each array has 10 cells.

C. Boundary Strategies

  • In relocation algorithm, behaviour of moving nodes while approaching to the given area’s boundaries (i.e. [xmin, xmax]× [ymin, ymax]) with respect to different boundary conditions should be taken into account.
  • Boundary strategies applied in [10] are adopted here which are non-stop at boundary, stop at boundary, wrap around.
  • (B1)-In non-stop at boundary, regardless of boundaries of given area, nodes relocate towards their new locations without limit.
  • (B2)-In stop at boundary, nodes stop at boundaries of given area and their movements are limited if their new computed locations are beyond the area boundaries.
  • (B3)-In wrap around, according to toroidal surface, nodes are wrapped around to other sides if new computed locations go beyond the area boundaries.

D. Angular Force Strategies

  • Angular force strategies in [10] based on exerted forces from node’s neighbours can be considered as:(A1)-Smallest Angular Movement Strategy, among exerted angular forces from node’s neighbours, the one is selected that causes smallest node angular movement.
  • (A2)-Closest Neighbour Movement Strategy, among exerted angular forces from nodes’ neighbours, the closest neighbour is selected as the exerting angular nodes.

E. Fuzzy Node Movement Algorithms

  • In their model, similar to [10], fuzzy node movement algorithms are as: Fuzzy radial movement (FRM)-.
  • Nodes are mutually affected by radial force from their neighbours.
  • The amount of node movement is related to overall push/pull virtual forces from their in-range neighbours.
  • Nodes exert a force to their in-range neighbours depending on aforementioned angular force strategies.
  • FRM then FAM (FRAM)- FAM is applied to result of FRM in consecutive iterations.

IV. PERFORMANCE METRICS

  • The performance metrics presented are: Percentage of Coverage(C)-Suppose that a 2-D rectangular area of [xmin, xmax] × [ymin, ymax] is divided into grid cells.
  • The coverage of the given grid cells is defined as the number of nodes covering the cells’ corner coordinates zi=(xi, yi).
  • Thus, percentage of 1-coverage is defined as the ratio of grid cells within range of at least one sensor node to the total number of area’s grid cells.
  • This metric illustrates how an efficient relocation algorithms are able to cover the given area.

V. RESULTS

  • The proposed node relocation algorithm was simulated by Matlab and N=100 nodes with the transmission and sensing range of Rc=Rs=15 are distributed uniformly in the rectangular 2-D space of [−100 100] × [−100 100]m2.
  • For the sake of brevity and page limit, only the result based on tuned fuzzy parameters with (ω1, ω2, ω3) = (0, 0, 1) (Equation 4), with zone range Rzone=1 ·Rc and A1 and B2 and movement algorithm of FRM are presented in Figure 3.
  • The rest of the results more or less follow the same trends.
  • Figure 3 also shows that proposed model either outperform or is comparable to DSSA for different movement strategies, even DSSA benefits from expected global node density.
  • It should be noted that depending on different linear combinations of weights (ω1,ω2, ω3) (Equation 4), performance of relocation algorithms with different movement strategies FRM, FAM, FRAM and FARM can vary.

VI. CONCLUSION AND FUTURE WORK

  • A tuned fuzzy logic relocation model is proposed in which its fuzzy parameters are tuned either globally or locally via particle swarm optimization technique so proper amount of virtual forces can be exerted on nodes.
  • The results show that their proposed model either outperform or closely matches the performance DSSA in terms of percentage of coverage, uniformity and average movement even the tuned parameters are obtained locally within nodes’ transmission range Rc.
  • As a possible extension, by using light PSO computations, instead of only the first relocation iteration, fuzzy parameters can continuously be tuned and modified in consecutive iterations in each node according to the neighbours behaviour in the given iteration.

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method and assumptions and in section IV and V respectively
performance metrics and result are presented. Finally in section
VI, conclusion and possible future work are presented.
II. RELATED WORK
A large proportion of relocation and movement algorithms
in the literature [3], [6], [7], [8], [9], [10], [11], [12], [13],
[14], [15] are devoted to currently deployed nodes in or-
der to give the network more flexibility, swiftness to react
autonomously in the environments where centralized control
and supervision are not feasible. Each of these algorithms
are aimed at different and overlapping goals such as network
connectivity [13], lifetime [12], re-alignment of unbalanced
deployments [7], coverage increase [7], recovery of small
and large scale coverage holes [6], [9], [14]. However, these
algorithms more or less would be able achieve other than their
primary objectives. Thus, the performance and efficacy of these
algorithms should also be investigated for applications other
than their primary design goals. As most algorithms partially
inspired and evolved from each other, it is hard to draw fine
line between them. They can be mainly classified into virtual
force-based (radial [7], [16] or angular [13]), voronoi-based
[14] and flip-based [6] movement algorithms. Among these
algorithms in WSNs, the amount of unnecessary movements,
oscillations and power exhaustion of nodes with local interac-
tions in the distributed relocation algorithms especially with a
harsh and hostile environments with lack of central supervision
and operation should be reduced as possible. In order to
save nodes’ power and to localize movement to a specific
area in the network, relocation algorithm can be applied to
a selected set of nodes [17], [18], fully or partially to avoid
unnecessary node oscillations or energy consumption caused
by careless movement strategies. Reduction in overhead and
delay of centralized relocation paradigm comes at the price
of increased uncertainty among autonomous nodes who have
local interactions within their ranges.
Although fuzzy logic relocation model shown to be candi-
date solution to address such a uncertainty for the autonomous
moving nodes [10], among indefinite choices, proper and
justifiable fuzzy parameters and membership functions should
be selected. In proposed model, the proper fuzzy parameters
in fuzzy logic relocation model can be obtained by applying
PSO technique locally with different ranges and globally over
the given deployed area. similar to [10] with different angular,
boundary conditions and movement strategies, the efficiency
and performance of the given model in terms of coverage,
uniformity and movement are also compared with distributed
Self-Spreading Algorithm (DSSA) [7] which benefited from
expected global node density.
III. M
ETHODS AND ASSUMPTIONS
With the given sensing range R
s
and transmission range
R
c
, sensor nodes are modeled as unite disk graphs (UDG)
and are bi-directionally connected when they reside within
their one another’s ranges. Nodes are randomly deployed in
2D rectangular field of [x
min
x
max
] × [y
min
y
max
] with the
uniform distribution. Nodes’ locations are known by either
centralized or distributed localization algorithms [19], [20].
Circular zone around the node is defined as a circle with radius
of R
zone
(R
zone
= k · R
c
) with the node in the center of
circle and are used to obtain the fuzzy parameters from nodes’
neighbours residing in the given zone via PSO.
TABLE I: Fuzzy Rules [10]
(a) Pair Radial Force
System
Distance Pressure
Very Far No Action(0)
Far Pull hard(-1)
Moderate Pull(-0.5)
Close Push(0.5)
Too Close Push Hard(1)
(b) Pair Angular Force Sys-
tem
Distance Pressure
Very Far Hard(1)
Far Medium(0.75)
Moderate Slow(0.5)
Close Very Slow(0.25)
Too Close Nothing (0)
TABLE II: Membership Functions
z-function
f
z
(x; a, b) =
1, x a
1 2
xa
ba
2
, a x
a+b
2
2
xb
ba
2
,
a+b
2
x b
0, x b
Symmetric Gaussian function
f
g
(x; σ, µ) = e
(xµ)
2
2σ
2
s-function
f
s
(x; c, d) =
0, x c
2
xc
dc
2
, c x
c+d
2
1 2
xd
dc
2
,
c+d
2
x d
1, x d
A. Fuzzy Logic Parameters
Fuzzy rule-based systems are applied in a variety of
research areas [21], [22]. For fuzzy control problems Takagi-
Sugeno (TS) [21] rule based systems briefly are described as
follows:
Rule R
j
: if x
1
is A
j1
and · · · and x
n
is A
jn
then y
j
= a
0j
+ a
1j
x
1
+ · · · + a
nj
x
n
(1)
where x = (x
1
, x
2
, ..., x
n
) is an n-dimensional input, A
nj
is
a fuzzy membership and y is a non-fuzzy output. Fuzzy rule
base system’s output is calculated from the following equation,
y =
p
j=1
µ
j
(x) · y
j
N
j=1
µ
j
(x)
, (2)
µ
j
(x) = µ
1j
(x) µ
2j
(x) · · · µ
nj
(x) (3)
p is the total number of rules. Similar to [10] two different
fuzzy inference systems are used: fuzzy radial pair force
and fuzzy angular force. Both fuzzy radial pair force system
and fuzzy angular force system have one input as distance
with 3 gaussian functions, one z-function and one s-function
memberships (Table II) and one crisp output, pressure which
can take the fuzzy values push hard, push, no action, pull
and pull hard. The rules of these systems are listed in Table
I. Membership function parameters a, b, c, d, µ, σ computed
using particle swarm optimization. Figure 1 is brought as the
example of respectively tuned radial and angular membership
functions for angular strategy A
1
, boundary condition B
2
and
movement strategy F RAM. Hence, fuzzy parameters can be
tuned using particle swarm optimization with regard to linear
weighted combinations of metrics in terms of percentage of
coverage, uniformity, and average movement equation 4.
F
= argmax
F
{w
1
· C(F ) w
2
· U (F ) w
3
· M (F )} (4)
w
1
, w
2
, w
3
are respectively weights for coverage (C), unifor-
mity (U), and average movement (M ). F is a set of fuzzy
parameters tuned by PSO with regard to the performance
weights. Thus, parameters can be tuned based on one or linear
combination of the metrics. The negative and positive signs

0 5 10 15
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Radial Distance(m)
Angular Strategy=1, Boundary Condition= 2, Movement Algs.= FARM, Rzone: 1Rc
Too Close
Close
Moderate
Far
Very Far
(a) Radial Membership
0 50 100 150 200 250 300 350
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Angle(degree)
Angular Strategy= 1, Boundary Conditions= 2, Movement Algs.= FRAM, Rzone= 1Rc
Too Close
Close
Moderate
Far
Very Far
(b) Angular Membership
Fig. 1: Radial and Angular Membership function
used where performance metrics should be minimized (i.e.
movement, uniformity) or maximized (i.e. coverage) respec-
tively. In order to tune parameters PSO is applied in two
different global and local zone range which are as follows:
In global range, PSO applied on all deployed nodes over
whole 2D rectangular field ([x
min
x
max
]×[y
min
y
max
]) while
in local zone-range, proportion of nodes N
sel
from set of
deployed nodes N
total
(N
sel
N
total
) are randomly selected
with uniform distribution. PSO is applied for each selected
node with a zone-range of R
zone
(R
zone
= k · R
c
) around
selected node by taking account node’s neighbours residing
within its R
zone
range. It should be noted that in both local and
global ranges, boundary conditions are considered in tuning
fuzzy parameters.
B. PSO structures
In this paper, the constriction coefficient PSO used similar
to the [23]. Thus,in this approach the velocity update equation
is as follows:
υ
ij(t+1)
= χ
υ
ij(t)
+ φ
1
y
ij(t)
x
ij(t)
+ φ
2
ˆy
ij(t)
x
ij(t)

(5)
y
ij
is the particle best and ˆy
ij
is the global best particles and,
χ =
2k
2 φ
φ(φ 4)
(6)
with φ = φ
1
+ φ
2
, φ
i
= c
i
r
i
i = 1, 2. Equation 6 is
used under the constraint that φ 4 and k, r
i
[0, 1]. The
parameter k in the equation 6 controls the exploration and
exploitation. For k 0, fast convergence is expected and for
k 1 we can expect slow convergence with high degree of
exploration [23]. Each particle consists of two arrays, which
one is related to the memberships of the pair force fuzzy
systems and another one is related to the memberships of
the angular force fuzzy systems. Each fuzzy system has 5
memberships and each membership is specified by its mean
and variance, therefore each array has 10 cells.
C. Boundary Strategies
In relocation algorithm, behaviour of moving nodes while
approaching to the given area’s boundaries (i.e. [x
min
, x
max
]×
Iteration i
Iteration i+1
Angular Force
Radial Force
Fig. 2: Fuzzy Node Movement Algorithms [10]
[y
min
, y
max
]) with respect to different boundary conditions
should be taken into account. Boundary strategies applied
in [10] are adopted here which are non-stop at boundary,
stop at boundary, wrap around. (B
1
)-In non-stop at boundary,
regardless of boundaries of given area, nodes relocate towards
their new locations without limit. (B
2
)-In stop at boundary,
nodes stop at boundaries of given area and their movements
are limited if their new computed locations are beyond the
area boundaries. (B
3
)-In wrap around, according to toroidal
surface, nodes are wrapped around to other (opposite) sides if
new computed locations go beyond the area boundaries.
D. Angular Force Strategies
Force exerting node n
fv
is considered as vertex of angle
α = (n
1
, n
fv
, n
2
) (0 < α 180
) with each pair of
its neighbours n
1
,n
2
. Angular force strategies in [10] based
on exerted forces from node’s neighbours can be considered
as:(A
1
)-Smallest Angular Movement Strategy, among exerted
angular forces from node’s neighbours, the one is selected
that causes smallest node angular movement. (A
2
)-Closest
Neighbour Movement Strategy, among exerted angular forces
from nodes’ neighbours, the closest neighbour is selected as
the exerting angular nodes.
E. Fuzzy Node Movement Algorithms
In our model, similar to [10], fuzzy node movement
algorithms are as: Fuzzy radial movement (FRM)- Nodes are
mutually affected by radial force from their neighbours. The
amount of node movement is related to overall push/pull virtual
forces from their in-range neighbours. Fuzzy angular Move-
ment (FAM)- Nodes exert a force to their in-range neighbours
depending on aforementioned angular force strategies. FRM
then FAM (FRAM)- FAM is applied to result of FRM in
consecutive iterations. (Figure 2). FAM then FRM (FARM)-
FRM is applied to the result of FAM in consecutive iterations
(Figure 2).
IV. P
ERFORMANCE METRICS
The performance metrics presented are: Percentage
of Coverage(C)-Suppose that a 2-D rectangular area of
[x
min
, x
max
] × [y
min
, y
max
] is divided into grid cells. The
coverage of the given grid cells is defined as the number
of nodes covering the cells’ corner coordinates z
i
=(x
i
, y
i
).
Thus, percentage of 1-coverage is defined as the ratio of
grid cells within range of at least one sensor node to the
total number of area’s grid cells. This metric illustrates how
an efficient relocation algorithms are able to cover the given
area. Uniformity (U )-The measure of nodes being uniformly

0 50 100 150 200 250 300 350 400 450 500
80
81
82
83
84
85
86
87
88
89
90
Number of Iterations
Percentage of 1−Coverage(100%)
# Experiments = 500, Boundary Condition = 2, Angular Strategy = 1
Movement Algorithm = FRM , [ω
1
ω
2
ω
3
] = [0 0 1]
DSSA
Global
R
zone
= 1R
c
R
zone
= 2R
c
R
zone
= 4R
c
(a) Percentage of 1-Coverage,(A
1
,B
2
,FRM)
0 50 100 150 200 250 300 350 400 450 500
10
−15
10
−10
10
−5
10
0
Number of Iterations
Uniformity
# Experiments = 500, Boundary Condition = 2, Angular Strategy = 1
Movement Algorithm = FRM , [ω
1
ω
2
ω
3
] = [0 0 1]
DSSA
Global
R
zone
= 1R
c
R
zone
= 2R
c
R
zone
= 4R
c
(b) Uniformity,(A
1
,B
2
,FRM)
0 50 100 150 200 250 300 350 400 450 500
10
−15
10
−10
10
−5
10
0
Number of Iterations
Average Movement(m)
# Experiments = 500, Boundary Condition = 2, Angular Strategy = 1
Movement Algorithm = FRM , [ω
1
ω
2
ω
3
] = [0 0 1]
DSSA
Global
R
zone
= 1R
c
R
zone
= 2R
c
R
zone
= 4R
c
(c) Average Movement,(A
1
,B
2
,FRM)
Fig. 3: Performance Comparison of Relocation Algorithm for
globally and locally (R
zone
={1,2,4R
c
) Tuned fuzzy param-
eters
distributed is defined in [7]. U is defined as the average local
standard deviation of internodal distances [7].
U
i
=
k
i
j=1
(D
i,j
M
i
)
2
k
i
1/2
, U =
N
i=1
U
i
N
, (7)
where N is the total number of nodes, k
i
is number of
neighbours of the ith node, D
i,j
is the distance between the ith
and jth nodes, and M
i
is the mean internodal distance between
the ith node and its neighbours [7]. Average Movement (M )
- It is defined as total movement of nodes in each iteration
over the number of nodes in the given iteration. As movement
is related to amount of node’s consumed energy, average
0 50 100 150 200 250 300 350 400 450 500
80
82
84
86
88
90
92
Number of Iterations
Percentage of 1−Coverage(100%)
# Experiments = 500, Boundary Condition = 2, Angular Strategy = 1
R
zone
= 1R
c
, [ω
1
ω
2
ω
3
] = [0 0 1]
DSSA
FRM
FAM
FRAM
FARM
(a) Percentage of 1-Coverage
0 50 100 150 200 250 300 350 400 450 500
10
−15
10
−10
10
−5
10
0
Number of Iterations
Uniformity
# Experiments = 500, Boundary Condition = 2, Angular Strategy = 1
R
zone
= 1R
c
, [ω
1
ω
2
ω
3
] = [0 0 1]
DSSA
FRM
FAM
FRAM
FARM
(b) Uniformity
0 50 100 150 200 250 300 350 400 450 500
10
−15
10
−10
10
−5
10
0
Number of Iterations
Average Movement(m)
# Experiments = 500, Boundary Condition = 2, Angular Strategy = 1
R
zone
= 1R
c
, [ω
1
ω
2
ω
3
] = [0 0 1]
DSSA
FRM
FAM
FRAM
FARM
(c) Average Movement
Fig. 4: Performance of Different Movement Strategies with
Boundary condition B
2
and Angular Force Strategy A
1
movement of nodes in each iteration represent a suitable metric
for comparison of various node relocation algorithms in the
context of energy efficiency.
V. RESULTS
The proposed node relocation algorithm was simulated
by Matlab and N =100 nodes with the transmission and
sensing range of R
c
=R
s
=15 are distributed uniformly in
the rectangular 2-D space of [100 100] × [100 100]m
2
.
The fuzzy parameters are obtained locally as N
sel
= 30 of
total deployed nodes N
tot
= 100 are randomly selected with
zone ranges of R
zone
=(1, 2, 4)·R
c
. The fuzzy parameters are
tuned via particle swarm optimization (k = 0.5, c
1
= 3,
c
2
= 3 equation 6) with boundary conditions of B
1
, B
2
,
B
3
and angular strategies of A
1
and A
2
. The membership
parameters are also obtained globally in rectangular field of

Citations
More filters

Journal ArticleDOI
TL;DR: Simulation results show that the proposed game theoretic approach is able to substantially increase network lifetime and maintain network coverage in the presence of random damage events, as compared with the prior counterpart(s).
Abstract: Coverage holes (CHs) can compromise the reliability and functionality of wireless sensor networks. The recovery of CHs is challenging, especially in distributed applications where sensors have little knowledge about other sensors’ actions. We propose a new game theoretic approach for recovering the CHs in a distributed manner. The key idea is that we formulate a potential game between the sensors, where each mobile sensor in the network only depends on local knowledge of its neighboring nodes and takes CH recovery actions recursively with global convergence. An appropriate combined action of physical relocation and sensing range adjustment can be taken by each sensor to reduce the CHs in an energy-efficient way. Simulation results show that the proposed game theoretic approach is able to substantially increase network lifetime and maintain network coverage in the presence of random damage events, as compared with the prior counterpart(s).

18 citations


Proceedings ArticleDOI
02 Jun 2014
TL;DR: A Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs) and the optimal T-S fuzzy models are suggested.
Abstract: This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.

8 citations


22 Aug 2013
TL;DR: It is shown here that DSSA is not able to fully recover large scale coverage holes even if all nodes participate in recovery process and relocate with sufficient number of iterations.
Abstract: Coverage holes as large scale en mass and correlated node failures in wireless sensor networks, not only disturb the normal operation and functionality of networks, but also may endanger network’s integrity. Recent trends to use relocation of currently deployed nodes have attracted attention especially where manual addition of nodes are neither feasible nor economical in many applications. The transition from centralized to distributed node relocation algorithm gradually paves away for applications in which nodes are deployed in harsh and hostile environments in absence of central supervision and control. Although, many different relocation algorithms have been devised to address their given applications’ challenges and requirements and they are efficient in reaching their design goals, they may not be similarly responsive to unpredicted and different circumstances may occur in the network. This paper, demonstrates one of such case, DSSA (Distributed Self-Spreading Algorithm) that is mainly applied for balancing node deployments and recovery of small coverage holes. It is shown here that DSSA is not able to fully recover large scale coverage holes even if all nodes participate in recovery process and relocate with sufficient number of iterations.

7 citations


Proceedings ArticleDOI
01 Jan 2015
TL;DR: A cooperative coverage hole recovery model is proposed which utilises the simple geometrical procedure of circle inversion, and the performance of the proposed model performance is compared with a force-based approach.
Abstract: Unlike sporadic node failures, coverage holes emerging from multiple temporally-correlated node failures can severely affect quality of service in a network and put the integrity of entire wireless sensor networks at risk. Conventional topology control schemes addressing such undesirable topological changes have usually overlooked the status of participating nodes in the recovery process with respect to the deployed sink node(s) in the network. In this paper, a cooperative coverage hole recovery model is proposed which utilises the simple geometrical procedure of circle inversion. In this model, autonomous nodes consider their distances to the deployed sink node(s) in addition to their local status, while relocating towards the coverage holes. By defining suitable metrics, the performance of our proposed model performance is compared with a force-based approach.

5 citations


Cites background from "A Tuned Fuzzy Logic Relocation Mode..."

  • ...By providing a degree of control over the coverage and connectivity of networks, topology control schemes using distributed node relocation algorithms are able to maintain or recover network integrity in networks subject to dynamic topological perturbation [6], [7], [16], [9], [4], [10], [17]....

    [...]


Proceedings ArticleDOI
Harpuneet Kaur1, Sandeep Sharma1Institutions (1)
01 Mar 2016
TL;DR: This paper throws light on renowned strategies used for sensor deployment as well as comparision of sensor deployment algorithms in order to improve the results of existing deployment techniques.
Abstract: WMSNs are extensively used in various fields such as environmental monitoring, survellience, national security and health care. Wireless networks are composed of sensor nodes having capability of sensing environmental conditions, sink node and connected via internet to remote controller. Maximun coverage is one of the basic objective of WMSNs. So the current document presents review as well as comparision of sensor deployment algorithms. It considers the various performance metrics such as time, conserved energy and distance travelled. This paper throws light on renowned strategies used for sensor deployment. So this study ends up with suitable future scope in order to improve the results of existing deployment techniques.

3 citations


References
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Journal ArticleDOI
TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
Abstract: This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

17,354 citations


"A Tuned Fuzzy Logic Relocation Mode..." refers background in this paper

  • ...As promising surveillance, detection and tracking solutions for many applications, wireless sensor networks (WSNs) are becoming more available and ubiquitous in recent years [1], [2]....

    [...]


Journal ArticleDOI
TL;DR: This survey presents a comprehensive review of the recent literature since the publication of a survey on sensor networks, and gives an overview of several new applications and then reviews the literature on various aspects of WSNs.
Abstract: A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.

5,311 citations


"A Tuned Fuzzy Logic Relocation Mode..." refers background in this paper

  • ...As promising surveillance, detection and tracking solutions for many applications, wireless sensor networks (WSNs) are becoming more available and ubiquitous in recent years [1], [2]....

    [...]


Journal ArticleDOI
TL;DR: An overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements are provided and a detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented.
Abstract: Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them.

1,792 citations


"A Tuned Fuzzy Logic Relocation Mode..." refers background in this paper

  • ...Nodes’ locations are known by either centralized or distributed localization algorithms [19], [20]....

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Proceedings ArticleDOI
M. Clerc1Institutions (1)
06 Jul 1999
TL;DR: A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter, and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.
Abstract: A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter. We define a "no-hope" convergence criterion and a "rehope" method so that, from time to time, the swarm re-initializes its position, according to some gradient estimations of the objective function and to the previous re-initialization (it means it has a kind of very rudimentary memory). We then study two different cases, a quite "easy" one (the Alpine function) and a "difficult" one (the Banana function), but both just in dimension two. The process is improved by taking into account the swarm gravity center (the "queen") and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.

1,493 citations


"A Tuned Fuzzy Logic Relocation Mode..." refers background or methods in this paper

  • ...For k ∼ 0, fast convergence is expected and for k ∼ 1 we can expect slow convergence with high degree of exploration [23]....

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  • ...In this paper, the constriction coefficient PSO used similar to the [23]....

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Performance
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No. of citations received by the Paper in previous years
YearCitations
20172
20163
20151
20141
20132