1.
Coverage and connectivity issues in wireless sensor networks: A survey
“
Amitabha Ghosh,
Sajal K. Das
”
Description:

I only read some basic contents of this paper, such as:
Introduction
Coverage and
connectivity
Preliminaries
Coverage based on exposure
Summary
:

Coverage and connectivity
together can be treated as a measure of quality
of service in a sensor network; it tells us how well each point
in the region
is covered and
h
ow accurate is the information gathered by the nodes.
T
hree types of coverage have been
deﬁned
:
Blanket Coverage
—
to achieve a static arrangement of nodes that maximizes the
detection rate of targets appearing in the sensing
ﬁ
eld,
Barrier Coverage
—
to achieve a static arrangement of nodes that minimizes the
probability of undetected intrusion through the barrier,
Sweep Coverage
—
to move a number of nodes across a sensing ﬁeld, such that it
addresses a speciﬁed balance between
maximizing the detection rate of events and
minimizing the number of missed detections per unit area.
Sensing Model:

Empirical observations suggest that the quality of sensing (sensitivity)
gradually attenuates with increasi
ng
distance. The sensitivity, S, of a sensor
s
i
at point P is
modeled as:
S (s
i
, P) = λ
/
d (s
i
, P)
α
Where
λ and α are sensor

dependent parameters and d (si, P) is the Euclidean distance
between the sensor and the
point.
I
n this model the sensing range for
each node is
conﬁned
within a circular disk of radius R
s
, and is commonly referred to as the sensing radius.
In this model, a quantity R
u
is deﬁned, such that when R
u
< R
s
, the probability that a node
would detect an object at a
distance less than or equal to (R
s
− R
u
) is one, and at a distance
greater than or equal
to (R
s
+ R
u
) is zero. In the interval (R
s
− R
u
, R
s
+ R
u
), an object will be
detected with probability p. The quantity
R
u
is a measure of uncertainty in sensor detectio
n.
Based on the probabilistic sensing model, the notion
of probabilistic coverage
of a point
P (
x
i
, y
i
) by a sensor si is deﬁned as follows:
C
xi yi
(s
i
) =
0,
Rs + Ru ≤ d(s
i
, P)
e
−
ωaβ
,
Rs − Ru <
d (
s
i
, P) < Rs + Ru
1,
Rs − Ru ≥
d (
s
i
, P)
Where
a =
d (
s
i
, P)−(Rs − Ru), and ω and β are parameters that measure the detection
probabilities when an object
is within a certain
distance from a node.
Communication Model
:

Each node S
i
is able to communicate only up to
a certain threshold
distance from itself, called the communication radius, denoted by Rc
i
.
Nodes can have
different communication ranges depending on their transmission power levels. Two nodes
S
i and
S
j
are able to communicate with each other if the Euclidean distance between them is
less than or equal to the minimum
of their communication radii
, i.e.,
When
d(si , sj ) ≤ min
Rc
i
, Rc
j
.
Coverage based on exposure
:

Two kinds of viewpoints exist in formulating the coverage
p
roblem:
(1) Worst

case coverage,
(2) Best

case coverage.
In
the worst

case coverage, the problem is formulated with the goal to ﬁnd a path through
the sensing region such
that, an object moving along that path has the least observability by
the nodes, and thus, the probability of detecting the
moving object is mini
mum.
I
n the best

case coverage problem formulation, the goal is to ﬁnd a path that has the
highest observability, and
therefore, an object moving along such a path will be most
probable to be detected.
2.
The Coverage Problem in a Wireless
Sensor Network:
“
Chi

Fu
Huang, Yu

Chee Tseng
”
Summary:
T
he coverage problem, reﬂects how well a sensor network is monitored or
tracked
by sensors. T
his paper, f
ormulate this problem as a deci
sion problem, whose
goal is to determine
whether every point in
the service area of the sensor netwo
rk is
covered by at least k sen
sors, where k is a predeﬁned value. The sensing ranges of
sensors
can be unit disks or non

unit disks.
This
present polynomial

time
algorithm, in
terms of the number
of sensors.
Applications of
the result include:
(i)
positioning applications,
(ii)
situations which
require stronger environmental monitoring capability,
(iii)
Scenarios
which impose more stringent fault

tolerant capability.
T
his paper, have proposed solutions to two v
ersions of the
coverage problem,
namely k

UC
(Unit

disk

Coverage)
and k

NC
(Non

unit

disk

Coverage)
, in a wireless
sensor
network.
It
model the coverage problem as a decision problem,
whose goal is
to determine whether e
ach location of the target sens
ing a
rea is sufficiently covered
or not. Rather than determining
the level of coverage of each location, our solutions
are based on
checking the perimeter of each sensor’s sensing range. Although
this
r
scheme
can give an exact answer in O(nd log d) time.
3.
Coverage Problems in Wireless Ad

hoc Sensor Networks :
“
Seapahn Meguerdichian, Farinaz Koushanfar, Miodrag Potkonjak, Mani B.
Srivastava
”
Summary:
This paper,
address one of the fundamental problems, namely coverage.
Coverage in general, answers the questions about quality of service (surveillance)
that can be provided by a particular sensor network.
It
defines
the coverage problem from several points of view
i
ncluding:
Deterministic
, statistical, worst and best
case
and present examples in each domain.
By combining computational geometry and graph theoretic techniques, specifically
the Voronoi diagram and graph search
algorithms,
it
establishes

optimal polyno
mial
time worst and average case algorithm for coverage calculation.
In most sensor networks, two seemingly contradictory, yet related viewpoints of
coverage exist:
worst and
best case coverage
.
In worst

case coverage, attempts are made to quantify the
quality of service by
finding areas of lower observability from senso
r nodes and detecting breach re
gions.
In best

case cov
erage, finding areas of high ob
servability from sensors and
identifying the best support and guidance regions are of primary concern
.
4.
Energy

aware Node Placement in Wireless Sensor Networks:
“
Peng Cheng, Chen

Nee Chuah , Xin Liu
”
Summary:
One of the main design issues for wireless sensor
networks is the
sensor
placement problem. T
his paper,
formulate a constrained multivariable nonlinear
programming problem to determine both the locations of the sensor nodes and
data transmission pattern.
The two objectives studied in the paper are to maximize the network lifetime and to
minimize the
application

specific total cost, given a finite number of sensor/
aggre

gation nodes in a region with certain coverage requirement.
In this paper, we will examine many

to

one wireless sensor networks, where
information collected from all nodes is
aggrega
ted to a sink node
or fusion center.
Nodes closer to the sink node have heavier traffic load, since they not only collect
data within their sensing range but relay data for nodes further away as well.
Such an unbalanced traffic load introduces an unev
en power consumption
d
istribution among different sensor nodes.
Since traffic load and power
c
onsumption of each node are location

dependent, the
lifetime of a sensor network can be limited by those nodes with heavier traffic load
and th
us greater power co
nsumption.
Hence, node placement schemes will have
considerable impact on the lifetime of the whole sensor network.
The
contributions are threefold.
First, it
formulate a constrained multivariable nonlinear programming problem to
determine
both the locations of the nodes and the data
transmission pattern
considering two objectives: maximize the network lifetime and minimize the
application

specific total cost, given a finite number of sensor/aggregation nodes
in a geographical coverage.
Second, we present two optimal placement strategies, together with performance
bounds, for linear networks, i.e., sensors deployed along a straight line.
Third, after exploring and understanding the fundamentals of a linear network,
it
extends the resu
lts to a more sophisticated planar network.
5.
Coverage planning of Wireless Sensors for Mobile Target Detection:
“
Edoardo Amaldi, Antonio Capone, Matteo Cesana, Ilario Filippini
Politecnico di Milano, Italy
”
Summary:
This paper
proposes
an optimization framework for selecting the positions
of wireless sensors to detect mob
ile targets travers
ing a given area.
T
he main contributions of this paper are the
following:
1)
An
optimization approach to the planning of
sensor positions wh
ere
coverage
quality, deﬁned accord
ing to the concept of exposure, depends on the Euclidean
distance from the intruder;
2)
Optimal
solution of different
planning problem versions based on MILP
f
ormulations;
3) An
efficient
heuristic algorithm that can be used to solve
large size instances in
reasonable time.
This paper
propose
s
and investigates two WSN
planning problems.
In the ﬁrst one, sensors must be positioned in order to maximi
ze the exposure of the
least ex
posed pat
h, subject to a budge
t on the installation cost (num
ber of sensors).
In the se
cond one, sensors have to be po
sitioned so as to minimize the installation
cost, provided
that the exposure of the least

exposed path is above a given
threshold.
C
overage
quality depends on the
distance from sensors and is measured by the path
exposure,
which quantiﬁes the capability of the network to detecting
a mobile object
moving along a given path.
6.
Mobility Improves Coverage of Sensor Networks
:
“
Benyuan
Liu
,
Peter Brass
,
Olivier Dousse
,
Philippe Nain
,
Don Towsley
”
Summary:
In this
paper, we study the dynamic aspects of the coverage of a
mobile
sensor network that depend on the process of sensor
movement. As time goes by, a
position is m
ore likely to be
covered; targets that might never be detected in a
stationary
sensor network can now be detected by moving sensors.
The main contributions of our work are:
First, we characterize the fraction of the area covered by
sensors for a randomly

deployed stationary sensor network.
This characterization shows how the covered
area depends
on the density and sensing characteristics of the sensors.
It
then
considers
a random mobility model for sensors
and
studies
the eﬀect of
sensor mobility on variou
s aspects of
network coverage.
W
e study the detection time of an intruder, which is deﬁned
to be the time elapsed
before the intruder is ﬁrst detected.
For mobile intruders, the detection time
depends on both
the sensor and intruder mobility strategies.
For a given sensor mobility
behaviour
, we
assume that an intruder can choose its
mobility strategy so
as to maximize its detecti
on time (its lifetime before be
ing
d
etected).
On the other hand, sensors choose a mobility
strategy that minimizes th
e maximum
detection time result
ing from the intruder’s mobility strategy.
This paper
proves
that the
optimal sensor mobility strategy is for each sensor to
c
hoose
its direction uniformly
at random. The corresponding in
truder mobility
strategy is to remain stationar
y in order to
maximize the time before it is detected.
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