Wireless Sensor Network, 2011, 3, 117-120
doi:10.4236/wsn.2011.34013 Published Online April 2011 (http://www.SciRP.org/journal/wsn)
A Practical Solution for Location Estimation in Manually
Deployed Wire less Sensor Networks
Om Prakash Sahu1, Tarun Dubey2
1Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India
2Department of Electronics and Communication Engineering, Invertis University, Bareilly, India
Received February 15, 2011; revised March 9, 2011; accepted March 15, 2011
Abstract
This paper addresses the existing researc h and adds another aspect o f functionality by incorporating pertinent
sensor nodes to provide a dynamic location discovery and estimation. The software used provides an easy gra-
phical user interface to visualize a particular location in accordance with geographical latitude and longitude.
A simple real time location estimation technique is worked out for wireless sensor networks based on manual
deployment of sensors. The proposed scheme finds more efficient solutions with less quantity of sensors as
compared to existing deployment schemes. The set up is evaluated exclusively in real environments using
IRIS sensor nodes supported by a global positioning system module to provide visualization of an outdoor
location. The results are offered by Google Earth application.
Keywords: Wireless Sensor Network (WSN), Global Positioning System (GPS), Localization, Sensor Node,
IRIS Sensor
1. Introduction
Wireless sensor networks have been identified as one of
the most prominent technologies for the 21st century [1].
With upcoming advancements, sensor networks find mor e
prosperous grounds to spread in areas where traditional
networks fail or are inadequate. They find applications in
a variety of areas such as climate monitoring, military
use, industry and sensing information from inhospitable
locations. Sensor networks are also being used for condi-
tional based maintenance, surveillance, computer augmen-
tation and inventory tracki ng. M a ny ot her appl ications are
yet to appear when the technology will become more
widely available. Unlike other networks, sensor networks
depend on deployment of sensors over a physical loca-
tion to fulfill a desired task. Dense deployments imply
the use of hundreds or thousands of sensor nodes in small
areas with tight coupling within their environments to
ensure effective coverage range in a geographical field.
Sensor networks also have various computational con-
straints like range management, topology selection, power
consumption and node localization. Although a lot of re-
search is being conducted, there are several open issues
for sensor networks such as signal processing [2], dep-
loyment [3], operating cost, localization and location es-
timation, since the potential for sensor networks in diverse
fields has been realized, various software frameworks are
being developed to make deployments relatively easy in
terms of configuration, maintenan c e and usability.
In this paper we present a solution for estimating the
location of sensor nodes in a manually framed WSN by
using Mote View 2.0.1F software. The software has a
complete computational s tack and provides a reliable I R I S
sensor platform for monitoring the location of the ma-
nually placed nodes in terms of latitude and longitude
values. The software architecture provides a customized
application with minimum errors. The nodes are executed
on a computer through a gateway connected to it. The
system is also equipped with Google Earth application for
enabling visualization.
The IRIS sensor nodes have open access to the out-
door environment as they are placed manually on a roof
top of a building; the exact location of the building or site
is obtained in terms of relative latitude and longitude. In-
door applications are not successful as the system re-
quires massive optimization to minimize performance de-
gradation due to walls and other objects found in indoor
environments because of the use of a GPS device. This
paper strongly focuses on localization and suggests a sim-
ple solution to localize sensors in a manually deployed

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WSN.
2. Related Work and Background
Recent research in WSNs strongly focuses on sensing
applications, unlike the applications that were proposed
in the previous years. The current perspective to deploy
sensors manually is used in several projects for real sys-
tem deployments [4]. Our proposed solution has mini-
mum overhead and it is cost effective interfacing be-
tween different networks is gaining importance as re-
search on WSNs related software environments is being
pursued during the last few years [5]. There are sources
providing references to a number of related solutions,
some of these approaches are quite similar to small sen-
sor networks with large systems. However, most of the
existing softwares deal with respective application sce-
narios and target domains. They choose a different ar-
chitecture for solving similar problems like fault detec-
tion and configuration of the network in real time [6].
Some configurations follow an N-tier architecture [7,8]
while others extend this concept using a peer-to-peer
logic [9] recently, BSPA was presented, in which some
nodes in the network are first selected and then formed
into a backbone network [10]. Methods to adjust the
directions overlap with the local coordinate systems and
an approach to merge them has also been proposed to
resolve the ambiguities. A graphical virtual instrumenta-
tion development platform to check data flow and inter-
face for real time data processing has also been pro-
posed [11].
For industrial and academic research, the inherent in-
teraction between sensor networks and the physical world
makes location awareness as one of the essential services
for emerging applications such as location based direc-
tory service [12] and entity tracking [13]. In addition to
the above work, a solution of walking GPS [14] is sug-
gested for use in real manual deployments of sensors to
evaluate the localization performance but projects some
errors within the range of few meters. In view of the
above cited limitation s that are exhibited by curren t loca-
lization techniques, we have proposed a practical solu-
tion to provide better accuracy in a running network with
less complexity, minimum sensors and meek cost.
3. System Description and Overview
The architecture of WSN, as shown in Figure 1, con-
sists of spatially distributed sensor nodes, which form a
multihop network to measure the parameters of interest
within the application domain. They communicate it to a
sink node or base station for access by the end user. Soft-
ware framework developed, makes deployments relative-
ly easy to configure and maintain. Mote View 2.0.1F ad-
dresses this issue of location estimation in a fine grained
fashion, having a set of tools to control the network and
provide data related to latitude and longitude. As de-
scribed earlier, a network of self powered IRIS sensor
nodes is deployed in a three-dimensional space. A moni-
toring or a sink node is responsible for collection of data
reports form individual sensor nodes is directly con-
nected with a personal computer. We do not assume any
specific routing or MAC protocol [15] for this network,
hence each node along with the sink, which has a unique
ID, and knows its location within the network. The nodes
configured in the network allow data transmission in hops
from one node to the other within the network and finally
to the base station or sink connected with the computer.
The transmission from nodes to the sink can take any po-
ssible path, because if any node fails or disables, the data
flow is not interrupted. Mote View 2.0.1F developed by
Crossbow [16] is used to program the nodes for d isplay-
ing the real time data to estimate the location using
Google Earth application, an online available software
tool.
Figure 1. System desc r i ption of a manually deployed wireless sensor network.

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4. Performance Evaluation and Deployment
A self-powered IRIS sensor node consists of a mote
(processor and radio) and a sensor board, programmed
with a unique node ID, is enclosed in casings and physi-
cally placed at different locations. A laptop or a personal
computer interfaced to gateway (MIB520) provides data
connectivity to the server through a port. The IRIS sen-
sor platform provides a wider coverage area, low power
consumption and better optimization for several other
applications and is based on IEEE 802.15.4 compliant
radios.
With an internet connection available, the user can
control the system remotely from any location as the
nodes are programmed and capable to transmit message
packets with a header. The program runn ing on the serv-
er collects and displays information of interest to th e user
through a wireless link. In order to achiev e real time live
data from sensor nodes, the base station has an inbuilt
GPS antenna to transmit the real time informatio n of lati-
tude and longitude points of the nodes placed on the roof-
top of the building. The gateway also allows bidirection-
al communication to organize the current information of
a specific geographic location (longitude and latitude).
To visualize the location in a 3D space, altitude can also
be incorporated as a third parameter but is not automati-
cally generated by the software under use, since most
environments use 2D representations, due to the advan-
tage of simultaneous display and network spanning across
individual buildings.
5. Experimental Results
The Mote View platform [17] enabled to administer the
nodes in terms of specific values for latitude and longi-
tude. The deployed IRIS sensor motes with sensing
modalities monitored the exact geographical location of
the individual building in terms of relative positions for
latitude as 28˚29'2217" N and longitude as 79˚49'4267"
E. The specific location of the site in instantaneous time
span and spatial form was correctly visualized with
Google Earth application through the site’s satellite im-
age with live data. The satellite image of the site where
the w ireless sensor n etwork was tested can be seen in Fi-
gure 2. A perfect match between the deployment loca-
tion and relative geographical position was observed.
However localization errors are not provided due to ir-
regular deployments and device limitati ons.
6. Conclusions and Future Scope of Work
The technique to deploy sensor nodes manually is cur-
rently used in several projects, and there are scenarios of
real system deployments, where manual deployment is
the only solution. Results show that the deployed nodes
estimate their relative latitude and long itude position s for
a reference point. The average localization error is mainly
due to the limitations of the devices used, because for lo-
cation information, the nodes completely rely on the glo-
bal positioning system and localizing themselves in the
middle of their proximate placements or reference points.
Figure 2. Screen shot image of the s ite obtained through Google Earth application.

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In future, performance evaluation and scalability will be
done through simulation. The experience from the cur-
rent deployment of the sensors can be used further to
address the aerial deployment. Considering the rate, alti-
tude, and trajectory of sensor nodes the actual location
information at the time of initial deployment, can also be
obtained using our solution, giving a starting point to-
wards a better and precise localization scheme. The au-
thors are working towards making it more common and
adaptable for other user communities also.
7. Acknowledgements
The authors would like to acknowledge the support and
assistance of Electronics and Communication Engineer-
ing Departments at National Institute of Tech nolog y, Ku-
rukshetra and Invertis University, Bareilly.
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