Towards Accurate Mobile
Sensor Network Localization in Noisy Environments
ABSTRACT:
The node localization problem in mobile
sensor networks has received significant attention. Recently, particle filters adapted
from robotics have produced good localization accuracies in conventional
settings. In spite of these successes, state of the art solutions suffer
significantly when used in challenging indoor and mobile environments
characterized by a high degree of radio signal irregularity. New solutions are
needed to address these challenges. We propose a fuzzy logic-based approach for
mobile node localization in challenging environments. Localization is
formulated as a fuzzy multi-lateration problem. For sparse networks with few
available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy
logic-based localization scheme is implemented in a simulator and compared to
state of the art solutions. Extensive simulation results demonstrate
improvements in the localization accuracy from 20% to 40% when the radio
irregularity is high. A hardware implementation running on Epic motes and
transported by iRobot mobile hosts confirms simulation results and extends them
to the real world.
EXISTING
SYSTEM:
Range-based localization methods require an estimate of the distance
or angle between two nodes to localize and may operate in both absolute and
relative coordinate systems.
Typical drawbacks for these methods
include higher computational loads, increased node size, higher energy consumption
and increased cost.
It assumes a fixed number of anchors but
handles mobility very well. The computation and refining are not suitable for a
resource-constrained computation platform like a MicaZ node.
Range-free localization methods are typically used in systems where
connectivity is the metric of choice and actual geographic distance is less
important. Hop counting is a technique frequently used in these scenarios,
where the distance between two nodes is inferred from the number of hops a
packet takes and is based on some assumed or measured average hop length.
A major drawback is that it fails in networks
with irregular topologies such as those with a concave shape. Mobility incurs
large overhead since all hop counts must be refreshed frequently.
PROPOSED
SYSTEM:
Fuzzy logic offers an inexpensive and
robust way to deal with highly complex and variable models of noisy, uncertain
environments. It provides a mechanism to learn about an environment in a way
that treats variability consistently.
Fuzzy logic can similarly be applied to
localization. Empirical measurements are made between participating anchors in predictable
encounters. These measurements are analyzed to produce rules that are used by
the fuzzy inference systems, which interpret RSS input from unlocalized nodes
and other anchors. The output of this process recovers the actual distance,
compensated for variability in the local environment. This basic technique is
employed in two constituent subsystems of FUZLOC - the Fuzzy Multilateration System
(FMS) and the Fuzzy Grid Prediction System (FGPS).
In our proposed fuzzy logic-based
localization system, distances between a mobile sensor node and anchor nodes
are fuzzified, and used, subsequently in a Fuzzy Multilateration procedure to
obtain a fuzzy location. In case two or more anchors are not available for
performing localization using fuzzy multilateration, the sensor node employs a
new technique, called fuzzy grid prediction, to obtain a location, albeit imprecise.
In the Fuzzy Grid Prediction method, the
node uses ranging information from any available anchor to compute distances to
several fictitious “virtual anchors” which are assumed to be located in
predetermined grids or quadrants. This allows the node to locate the
grid/quadrant in which it is present. In conventional localization schemes, the
location of a node is typically represented by two coordinates that uniquely
identify a single point within some two-dimensional area. Localization using
fuzzy coordinates follows a similar convention. The two dimensional location of
a node is represented as a pair (X, Y ), where both X and Y are
fuzzy numbers and explained below. However, instead of a single point, the
fuzzy location represents an area where the probability of finding the node is
highest, as depicted in Figure
MODULES:
·
A FUZZY
LOGIC-BASED NODE LOCALIZATION FRAMEWORK Module
·
Fuzzy
Multilateration Module
·
Fuzzy
Inference Module
·
System
Implementation Validation Module
MODULE
DESCRIPTION:
A FUZZY LOGIC-BASED NODE LOCALIZATION FRAMEWORK
Module
In this module, we develope a scenario
with highly irregular radio ranges, typical of harsh indoor or extremely
obstructed outdoor environments. The irregularity in the radio range is modeled
in these simulators as a degree of irregularity (DoI) parameter. The DoI
represents the maximum radio range variation per unit degree change in direction.
We define a harsh environment as one in
which the distance between sender and receiver cannot be accurately determined
from the RSS alone, due to environmental phenomena such as multipath
propagation and interference
For more complete problem formulation
we mention that the aforementioned localization techniques assume that
given a set of mobile sensor nodes, a subset of nodes, called anchors, know
their location in a 2-dimensional plane. Also, nodes and anchors move randomly
in the deployment area. Maximum velocity of a node is bounded but the actual
velocity is unknown to nodes or anchors. Nodes do not
have any knowledge of the mobility
model. Anchors periodically broadcast their locations. All nodes are deployed
in a noisy, harsh environment and they do not have any additional sensors
except their radios.
Fuzzy Multilateration Module:
We present fuzzy multilateration, a
component of our fuzzy inference process, which obtains a node’s location from
noisy RSS measurements, using fuzzy rule sets.
Fuzzy Inference Module
We present a fuzzy grid prediction
scheme, which optimizes our fuzzy inference process, under conditions of low
anchor density. However, in mobile sensor networks with low anchor densities, it
might frequently be the case that a node does not have enough anchors for
multilateration. To address this problem we extend our fuzzy logic-based localization
framework to predict an area, e.g., a cell in a grid, where the node might be.
The idea is inspired from cellular systems [21]. We propose to virtualize the
anchors, so that a node is within a set of Virtual Anchors at any point in
time. A Virtual Anchor is a fictitious anchor which is assumed to located at a known,
fixed location in the field of deployment, the distance to which can be found
in an approximate way from the node. In FUZLOC, we place virtual anchors at the
center of every square cell that the field is divided into, as described below.
The key idea is that the nearer a node is to a virtual anchor, the more likely
it is that the node can be found in that cell.
System Implementation Validation Module
We perform extensive simulations and
compare our solution with to state of the art algorithms, using both real-world
and synthetic data.
LITERATURE SURVEY:
1 ) Deploying a wireless sensor network on an active volcano
Authors:
Konrad Lorincz , Matt Welsh , Omar Marcillo , Jeff Johnson , Mario Ruiz ,
Jonathan Lees
Augmenting heavy and power-hungry data collection
equipment with lighter, smaller wireless sensor network nodes leads to
faster,larger deployments. Arrays comprising dozens of wireless sensor nodes
are now possible,allowing scientific studies that aren’t feasible with
traditional instrumentation. Designing sensor networks to support volcanic
studies requires addressing the high data rates and high data fidelity these
studies demand. The authors ’ sensor-network application for volcanic data
collection relies on triggered event detection and reliable data retrieval to
meet bandwidth and data-quality demands. Wireless sensor networks — in which
numerous resource-limited nodes are linked via low-bandwidth wireless radios —
have been the focus of intense research during the past few years. Since their
conception, they’ve excited a range of scientific communities because of their
potential to facilitate data acquisition and scientific studies. Collaborations
between computer scientists and other domain scientists have produced networks
that can record data at a scale and resolution not previously possible. Taking
this progress one step further, wireless sensor networks can potentially
advance the pursuit of geophysical studies of volcanic activity. Two years ago,
our team of computer scientists at Harvard University began collaborating with
volcanologists at the University of North Carolina, the University of New
Hampshire, and the Instituto
2)
Distressnet: a wireless ad hoc and sensor network architecture for situation
management in disaster response
AUTHORS:
George, S.M. Texas A&M Univ.,
College Station, TX, USA Wei Zhou
; Chenji, H. ; Myounggyu Won ; Yong Oh Lee ;
Pazarloglou, A. ; Stoleru, R. ; Barooah, P.
Situational awareness in a disaster is
critical to effective response. Disaster responders require timely delivery of
high volumes of accurate data to make correct decisions. To meet these needs,
we present DistressNet, an ad hoc wireless architecture that supports disaster
response with distributed collaborative sensing, topology-aware routing using a
multichannel protocol, and accurate resource localization. Sensing suites use
collaborative and distributed mechanisms to optimize data collection and
minimize total energy use. Message delivery is aided by novel topology
management, while congestion is minimized through the use of mediated
multichannel radio protocols. Estimation techniques improve localization
accuracy in difficult environments.
3)
VigilNet: an integrated sensor network system for energy-efficient surveillance
AUTHORS:
ian He , Sudha Krishnamurthy , Liqian Luo , Ting Yan , Lin Gu , Radu Stoleru ,
Gang Zhou , Qing Cao , Pascal Vicaire , John A. Stankovic , Tarek F. Abdelzaher
, Jonathan Hui , Bruce Krogh , Tianhe@cs. Umn. Edu S. Krishnamurthy , Liqian
Luo , T. Yan , L. Gu , R. Stoleru , G. Zhou , Qing Cao
This article describes one of the major
efforts in the sensor network community to build an integrated sensor network
system for surveillance missions. The focus of this effort is to acquire and
verify information about enemy capabilities and positions of hostile targets.
Such missions often involve a high element of risk for human personnel and
require a high degree of stealthiness. Hence, the ability to deploy unmanned
surveillance missions, by using wireless sensor networks, is of great practical
importance for the military. Because of the energy constraints of sensor
devices, such systems necessitate an energy-aware design to ensure the
longevity of surveillance missions. Solutions proposed recently for this type
of system show promising results through simulations. However, the simplified
assumptions they make about the system in the simulator often do not hold well
in practice, and energy consumption is narrowly accounted for within a single
protocol. In this article, we describe the design and implementation of a
complete running system, called VigilNet, for energyefficient surveillance. The
VigilNet allows a group of cooperating sensor devices to detect and track the
positions of moving vehicles in an energy-efficient and stealthy manner. We
evaluate VigilNet middleware components and integrated system extensively on a
network of 70 MICA2 motes. Our results show that our surveillance strategy is
adaptable and achieves a significant extension of
4)
Efficient geo-tracking and adaptive routing of mobile assets
AUTHORS: Balakrishnan, D. SITE, Univ. of Ottawa, Ottawa, ON, Canada
Nayak, A. ; Dhar, P. ; Kaul, S.
The recent advancements in technologies
such as cellular networks, wireless sensor networks (WSN), radio-frequency
identification (RFID), and global positioning system (GPS) have lead us to
develop a realistic approach to tracking mobile assets. Tracking and managing
the dynamic location of mobile assets is critical for many organizations with
mobile resources. Current tracking systems are costly and inefficient over
wireless data transmission systems where cost is based on the rate of data
being sent. Thus our main research goal is to develop efficient and improved
asset tracking solutions and consume valuable mobile resources. In addition, we
also adapt their routes by means of a novel and efficient geographical tracking
approach that performs route adaptation. We focus on tracking GPS-enabled
mobile devices mounted on the asset by understanding the behavior of a mobile
device for reporting GPS data in various demographics. This paper is
complemented with result evaluations based on a simulation environment with
real logs.
5)
GPSfree node localization in mobile wireless sensor networks
AUTHORS:
Hüseyin Akcan Polytechnic University Vassil Kriakov Polytechnic University
Hervé Brönnimann Polytechnic University Alex Delis
An important problem in mobile ad-hoc
wireless sensor networks is the localization of individual nodes, i.e., each
node's awareness of its position relative to the network. In this paper, we
introduce a variant of this problem (directional localization) where each node
must be aware of both its position and orientation relative to the network.
This variant is especially relevant for the applications in which mobile nodes
in a sensor network are required to move in a collaborative manner. Using
global positioning systems for localization in large scale sensor networks is
not cost effective and may be impractical in enclosed spaces. On the other
hand, a set of pre-existing anchors with globally known positions may not
always be available. To address these issues, in this work we propose an
algorithm for directional node localization based on relative motion of
neighboring nodes in an ad-hoc sensor network without an infrastructure of
global positioning systems (GPS), anchor points, or even mobile seeds with
known locations. Through simulation studies, we demonstrate that our algorithm
scales well for large numbers of nodes and provides convergent localization
over time, even with errors introduced by motion actuators and distance
measurements. Furthermore, based on our localization algorithm, we introduce
mechanisms to preserve network formation during directed mobility in mobile
sensor networks. Our simulations confirm that, in a number of realistic
scenarios, our algorithm provides for a mobile sensor network that is stable
over time irrespective of speed, while using only constant storage per
neighbor.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard Disk :
40 GB.
•
Floppy Drive : 1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse :
Logitech.
•
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
•
Operating
system : Windows XP.
•
Coding
Language
: C#.NET
•
Data
Base :
SQL Server 2005
•
REPORT : EXCEL 2007
REFERENCE:
Harsha Chenji and Radu Stoleru, “Towards
Accurate Mobile Sensor Network Localization in Noisy Environments”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL.
X, NO. X, JANUARY 2012.
