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Probabilistic Coveragein Wireless Sensor Networks
Authors : Nadeem Ahmed, Salil S. Kanhere, Sanjay Jha
Presenter : Hyeon, Seung-Il
![Page 2: Probabilistic Coverage in Wireless Sensor Networks Authors : Nadeem Ahmed, Salil S. Kanhere, Sanjay Jha Presenter : Hyeon, Seung-Il.](https://reader035.fdocuments.in/reader035/viewer/2022070402/56649f225503460f94c3ae1e/html5/thumbnails/2.jpg)
Contents
IntroductionRelated WorkTechnical PreliminariesProbabilistic Coverage AlgorithmSimulation SetupConclusion and Future Work
2008 Advanced Ubiquitous Computing2
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Introduction
Wireless sensor networks differ from ad-hoc networks in several ways
Introduction of the sensing component Performing two demanding tasks simultaneously, sensing
and communicating with each other
The sensing coverage is usually assumed uniform in all direction
The binary detection model Unrealistic assumption
The sensing capabilities are affected by environmental factors
It is imperative to have practical considerations at the design
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Introduction
This paper presented, Explore the problem of determining the coverage Capture the real world sensing characteristics of sensor
nodes using the probabilistic model Based on the path loss log normal shadowing model
Propose the Probabilistic Coverage Algorithm (PCA) Extension of the perimeter coverage algorithm
Simulation results Coverage calculated using probabilistic coverage algorithm
is more accurate than the idealistic binary detection model
2008 Advanced Ubiquitous Computing4
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Related Work
Most of the coverage related protocols assume uniform sensing rangesDifferent from above research in several ways
Computational geometry based approach Coverage is calculated at perimeter of each node
sensing circles This approach is truly distributed
Added advantage of being scalable and robust to failures
Differ from the perimeter coverage algorithm Sensing capabilities in all directions is always
probabilistic The detection probability depends on the relative
position of the event/target from the sensor
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Technical Preliminaries
Using the log-normal shadowing modelThe path loss PL(in dB) at a distance d is
n and Xσ can be measured experimentally
Also, PL(d0) can be measured experimentally2008 Advanced Ubiquitous
Computing6
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Technical Preliminaries
Each sensor has a receive threshold value γ Describes the minimum signal strength For a given transmit power and receive threshold value,
we can calculate the probability of receiving a signal at a given distance (=d) using Equations (2) and (4)
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Technical Preliminaries
The decrease in detection probability for a sensor based on shadowing model for parameters shown in
Table 1
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Technical Preliminaries
A point in the target region can be covered by more than a single sensor
Find the product of the individual detection probabilities of all sensors receiving the event occurring at that point
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Probabilistic Coverage Algorithm
The coverage depends on these: The sensing capabilities of the sensor The event characteristics
The transmit power and the receive threshold of sensors are known valuePT (Table 2) can be precomputed using Equation 1-4
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Probabilistic Coverage Algorithm
Definition 1 Effective coverage range, Reffec, of a sensor Si is defined
as distance of the target from the sensor beyond which the detection probability is negligible
Reffec is taken as the distance at which the probability of detection falls below 0.1
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Probabilistic Coverage Algorithm
The cumulative detection probability for two neighbors in a region, for parameters listed in Table 1
The cumulative detection probability is higher if neighbor sensors are located near each other
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Probabilistic Coverage Algorithm
Definition 2 A location in region A is said to be sufficiently covered if
its cumulative detection probability, due to sensors located within the effective coverage range Reffec of this location, is equal to or greater than DDP, the detection probability desired by the application
Objective is to check whether all locations in the given region are sufficiently covered or not
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Probabilistic Coverage Algorithm
Make the following assumptions for this work Sensors are randomly deployed in the field Location information is available to each sensor node Communication range of sensors is at least twice the
effective coverage range, Reffec
Sensors can detect boundary of the region if the boundary is within a sensor’s Reffec
Transmit power of target Pt and receive threshold γ for a sensor are known and γ is the same for all the sensors
Mean values of path loss component n and shadowing deviation σ are assumed for all the sensors
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Probabilistic Coverage Algorithm
PCA A node Si receives location information from all of its
one hop communication neighbors Si has two sensing circles with radius dreqd and deval
Node Si first detects whether it is within vicinity of the region boundary
The segments on perimeter that lie outside the region boundary are assigned detection probability of 1
Sensor do not need to calculate coverage for this part Next, neighbor contribution towards detection
probability is calculated Paper only consider neighbor contribution from nodes
within a distance of 2 * deval
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The value of distance increment being a tradeoff between the computational time and detection granularity
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Probabilistic Coverage Algorithm
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Probabilistic Coverage Algorithm
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Probabilistic Coverage Algorithm
Line 10-14 explained by Figure 5
Definition 3 If the perimeter of a sensor Si circle with radius deval is
covered by cumulative detection probability ρreqd, the region inside the circle is sufficiently covered with detection probability at least ρreqd
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Probabilistic Coverage Algorithm
Theorem 1 The whole region A is sufficiently covered by ρreqd if all
sensors in the region has perimeter, of circle with radius deval(> dreqd), sufficiently covered with detection probability ρ ≥ DDP
Following Theorem 1, If all the sensors report sufficiently covered perimeters at
Ci(deval), the whole region is sufficiently covered The information from all sensors describe the current
state of area coverage supported by the sensor network It can be utilized to deploy more sensors in the topology
Coverage hole detection or to guide mobility capable redundant nodes to specific
locations to satisfy the detection probability constraint
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Simulation Setup
Implemented in NS2 simulatordreqd is 6m for ρreqd 0.9, deval is 9m for ρeval 0.655
The PCA provides a more granular and accureate estimate of the coverage and detection probability
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Conclusion and Future Work
Proposed a probabilistic coverage algorithm To estimate area coverage in a randomly deployed WSN
The algorithm Adopts a probabilistic approachSimulation shows effectiveness of the algorithmRelax some of the assumptions
Mean values of path loss component, n The shadowing deviation, σ n and σ varies spatially as well as temporally due to
changing environments Multiple coverage constraint
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