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ENHANCED COMPOSITE APPROACH WITH MOBILE BEACON SHORTEST PATH TO SOLVE LOCALIZATION PROBLEM IN WIRELESS SENSOR NETWORKS

by KUMAR, SUNIL


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Nodes can utilize a global positioning system, but this solution is typically very
costly. Many researchers are focusing on designing different algorithm but
paying less attention on range measurement inaccuracy Localization is usually
carried out by measuring certain distance dependent parameters of wireless
radio link between the localization node and different localization base stations.
The parameters can be measured at the localization node or at the localization
base station. The traveling time of a signal between the localization node and
localization base station can easily be calculated. The time is proportional to the
distance of them and it can be used as parameter of localization. It is referred to
as time of arrival. There are many different algorithms to resolve these
challenged, like DV hop, mobile beacon and area of angle. Every algorithm has
its own merits and demerits. Some algorithms are best for accuracy and some
for low cost, but it is difficult to get low cost and high accuracy at the same time
from a single algorithm.

There are two situations when this problem gets more critical; when there are
too many nodes in network and when the environment is hazardous. No single
method has yet been adapted for outdoor environments
Many services are provided to users on the basis of location in wireless sensor
networks. The role of location is very important in the wireless sensor networks.
To access the data location is very important as the data itself. Location is also
important for the upcoming areas such as ubiquitous computing, mobile
services, network planning and sensor networks [8].
There are different location techniques in which the most promising is GPS.
GPS works in outdoor environments, but it requires reception of satellite
signals. GPS receivers are also very costly as compared to laptops and sensor
nodes.
The widespread use of wireless networks in enterprise and commercial
establishments has also improved. Wi-Fi is used as an underlying technology to
estimate the location of the sensor nodes. Tags are used to store information in

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wireless sensor networks. The tags send updated location information to a
central database, at the same time when the equipment is moved.

Figure 1.4: Localization service middleware
Another area where a user is granted access to network resources is location
based access control such as database access based on the wireless user [8].
Localization is an integral part of most sensor networks where the data collected
is mapped to its originating physical location. For example consider Zebra Net,
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a sensor network which is used to monitor the migration of zebras. Here the
sensor nodes were strapped on zebras to take periodic measurement of their
location and relevant biometric data [8].
Middleware is software that provides a link between separate software
applications. Middleware is sometimes called plumbing because it connects two
applications and passes data between them. Middleware allows data contained
in one database to be accessed through another. This definition would fit
enterprise application integration and data integration software. Object web
defines middleware as: "The software layer that lies between the operating
system and applications on each side of a distributed computing system in a
network.
Use of middleware:

Middleware services provide a more functional set of application programming
interfaces to allow an application to:

Locate transparently across the network, thus providing interaction with

another service or application

Filter data to make them friendly usable or public via anonymization

process for privacy protection (for example)

Be independent from network services
Be reliable and always available
Add complementary attributes like semantics

Localization service middleware we can see in figure 1.3.

1.5 APPROACHES TECHNIQUES IN WIRELESS SENSOR NETWORK
Existing location discovery approaches basically consists of two basic phases:
(1) distance or angle estimation and (2) distance and angle combining. The most
popular methods for estimating the distance between two nodes are described
below:
1.5.1 Received Signal Strength Indicator (RSSI)
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Received signal strength indicator (RSSI) is a measurement of the power
present in a received radio signal. RSSI is generic radio receiver technology
metric, which is usually invisible to the user of the device containing the
receiver, but is directly known to users of wireless networking of IEEE 802.11
protocol family. RSSI measures the power of the signal at the receiver and
based on the known transmit power, the effective propagation loss can be
calculated. RSSI is often done in the intermediate frequency (IF) stage before
the IF amplifier. In zero-IF systems, it is done in the baseband signal chain,
before the base band amplifier. RSSI output is often a DC analog level. It can
also be sampled by an internal ADC and the resulting codes available directly or
via peripheral or internal processor bus, next by using theoretical and empirical
models we can translate this loss into a distance estimate. This method has been
used mainly for RF signals. RSSI is a relatively cheap solution without any
extra devices, as all sensor nodes are likely to have radios. The performance,
however, is not as good as other ranging techniques due to the multi path
propagation of radio signals. In [26], the authors characterize the limits of a
variety of approaches to indoor localization using signal strengths from 802.11
routers. They also suggest that adding additional hardware or altering the model
of the environment is the only alternative to improve the localization
performance.
1.5.2 Time based methods (ToA, TDoA)
These methods record the time-of-arrival (ToA) or time-difference-of-arrival
(TDoA). The propagation time can be directly translated into distance, based on
the known signal propagation speed. These methods can be applied to many
different signals, such as RF, acoustic, infrared and ultrasound. TDoA methods
are impressively accurate under line-of-sight conditions. But this line-of-sight
condition is difficult to meet in some environments. Furthermore, the speed of
sound in air varies with air temperature and humidity, which introduce
inaccuracy into distance estimation. Acoustic signals also show multi-path
propagation effects that may impact the accuracy of signal detection.
1.5.3 Angle-of-Arrival (AoA)
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Angle of arrival is a method for determining the direction of propagation of a
radio frequency wave incident on an antenna array. AoA estimates the angle at
which signals are received and use simple geometric relationships to calculate
node positions. Generally, AoA techniques provide more accurate localization
result than RSSI based techniques but the cost of hardware of very high in AoA.
For the combining phase, the most popular alternatives are:

Figure 1.5: Localization techniques (a) Hyperbolic trilateration (b)
Triangulation (c) Maximum Likelihood Estimation

1.5.4 Hyperbolic Trilateration:
The most basic and intuitive method is called hyperbolic trilateration. It locates
a node by calculating the intersection of 3 circles as shown in Fig. 1.5(a).
1.5.5 Triangulation:
This method is used when the direction of the node instead of the distance is
estimated, as in AoA systems. The node positions are calculated in this case by
using the trigonometry laws of sines and cosines (shown in Fig. 1.5(b)).
1.5.6 Maximum Likelihood (ML) estimation:
ML estimation estimates the position of a node by minimizing the differences
between the measured distances and estimated distances shown in Fig. 1.5(c).

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1.6 LOCALIZATION AREA OF DEPLOYMENT
There are different localization schemes for wireless networks of different
deployment area. This is because of the difference in network topology, number
of users, and available resources for such networks. The localization can be
classified in to three types depending on the deployment area and type of
wireless sensor network.
1.6.1 Wide area localization
This technique is used for outdoor deployment. Wide area localization is long
range localization and there is problem of power but it is also a more expensive,
used in cellular network. Global positioning system (GPS) can be used for this
purpose most of the time, but sometime cellular network infrastructure also be
used.
Localization can be performed from reading at least three base stations. Local
base transceivers are base stations and cellular phone is local network. In
indoor environment it is difficult to maintain accuracy. We mostly use time of
arrival technique in cellular network for localization.
1.6.2 Local Area Localization
Local area localization is used for indoor implementation. The size of
localization area is very small due to that it is very cheap. Wireless access points
work as local base station while devices such as laptops and personal handheld
devices work as local network. GPS can not be used in indoor environment. We
use received signal strength (RSSI) technique to solve localization problem.
This technique is based on distance varying parameter so it is not robust in
complex, environment due to multipath and movement of people environment.
1.6.3 Ad – hoc Localization

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Ad-hoc network is heterogeneous and more power constrained. There is need of
some algorithms for this kind of network. The consumption is very low and low
cost can be obtained for communication. The nodes can be located with respect
to location of reference nodes, also known as anchors node which could be
movable or static in wireless sensor network.
1.7 COMPUTATIONAL MODEL FOR NETWORK MANAGEMENT
Research on localization in wireless sensor networks can be classified into two
broad categories. Each approach may be appropriate for a different application,
Centralized approaches require routing and leader election, fully distributed
approach does not have this requirement.

(a) (b)

(c)

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Figure 1.7: Computational model (a) centralized approach (b) locally
centralized approach (c) distributed approach

1.7.1 Centralized Localization:
Centralized localization is basically migration of inter-node ranging and
connectivity data to a sufficiently powerful central base station and then the
migration of resulting locations back to respective nodes. The advantage of
centralized algorithms are that it eliminates the problem of computation in each
node, at the same time the limitations lie in the communication cost of moving
data back to the base station. As representative proposals in this category [5, 6,
7] are explained in greater detail.
1.7.2 Distributed Localization:
In Distributed localizations all the relevant computations are done on the sensor
nodes themselves and the nodes communicate with each other to get their
positions in a network.
1.7.3 Locally centralized approach: This approach is based on cluster method
where in each cluster a node works as a master or base station for that cluster
and compute the information for other nodes figure 1.b.

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1.8 CLASSIFICATION OF APPROACHES IN WIRELESS SENSOR
NETWORK
Localization is classified mainly three categories according to the computation
in wireless sensor network as specified above and in figure also. Further each
category divided in to corresponding methods to solve localization problem.

Localization in WSN

Centralized Locally centralized Distributed

MDS-MAP

Annealin
g

Error propagation
aware localization

RSSI based

Diffusion

Beacon
based

Bounding Box

Gradient

Relaxation
based

Cooperativ
e App.

Spring model

Coordinate
switching DL
Interface metric
ranging based
localization

Cluster based

Hybrid
localization

Hybrid absolute
relative positioning
Composition of
inductive &
deductive approach

Figure 1.8: classifications of localization approaches in wireless sensor
networks

1.9 ISSUES AND CHALLENGES IN LOCATION DISCOVERY
1.9.1 Current Issues:
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1.9.1.1- Resource constraints: nodes must be cheap to fabricate, and trivially
easy to deploy. Nodes must be cheap, since fifty cents of additional cost per
node translates to $500 for a one thousand node network. Deployment must be
easy as well: thirty seconds of handling time per node to prepare for localization
translates to over eight man-hours of work to deploy a 1000 node network. That
means designers must actively work to minimize the power cost, hardware cost,
and deployment cost of their localization algorithms.
1.9.1.2- Node density: Many localization algorithms are sensitive to node
density. For instance, hop count based schemes generally require high node
density so that the hop count approximation for distance is accurate . Similarly,
algorithms that depend on beacon nodes fail when the beacon density is not high
enough in a particular region. Thus, when designing or analyzing an algorithm,
it is important to notice the algorithm’s implicit density assumptions, since high
node density can sometimes be expensive if not totally infeasible
1.9.1.3- Environmental obstacles and terrain irregularities: Environmental
obstacles and terrain irregularities can also wreak havoc on localization. Large
rocks can occlude line of sight, preventing TDoA ranging, or interfere with
radios, introducing error into RSSI ranges and producing incorrect hop count
ranges. Indoors, natural features like walls can impede measurements as well.
All of these issues are likely to come up in real deployments, so localization
systems should be able to cope.
1.9.1.4- Security: Security is the main issue in localization as the data is
transferred from beacon node to anchor node then any of mobile beacons which
is a virus or not secure acting as original mobile beacons transmit false
messages due to this an error will occur which is harmful for our computation.
1.9.1.5- Non convex topologies: Border nodes are a problem because less
information is available about them and that information is of lower quality.
This problem is exacerbated when a sensor network has a non-convex shape:
Sensors outside the main convex body of the network can often prove
unlocalizable. Even when locations can be found, the results tend to feature
disproportionate error.
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