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Data Fusion Models in WSNs: Comparison and Analysis.

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Abstract—In WSNs, hundreds of sensors collect data from the environment but these sensors have limited energy. Therefore, energy consumption is a very challenging issue in the design of WSNs. Sometimes, sensors fail as they got affected by the pressure or temperature. Such failure can lead to misleading measurements which in turn are waste of energy. As a result, data fusion is needed to overcome such confusion where it assures data’s efficiency and eliminates data’s redundancy. This paper provides an analysis of the state-of-the-art data fusion models along with their architectures. It also presents a comparison between these models to highlight the main objectives of each. In addition, it analyzes the advantages and the limitation of these models.
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1
AbstractIn WSNs, hundreds of sensors collect data from the
environment but these sensors have limited energy. Therefore,
energy consumption is a very challenging issue in the design of
WSNs. Sometimes, sensors fail as they got affected by the
pressure or temperature. Such failure can lead to misleading
measurements which in turn are waste of energy. As a result, data
fusion is needed to overcome such confusion where it assures
data’s efficiency and eliminates data’s redundancy. This paper
provides an analysis of the state-of-the-art data fusion models
along with their architectures. It also presents a comparison
between these models to highlight the main objectives of each. In
addition, it analyzes the advantages and the limitation of these
models.
Index Terms Wireless Sensor Networks (WSNs); Data
Fusion; Data Fusion Models; JDL Model; OODA Model;
Intelligence Cycle Model; Omnibus Model; Object-Oriented
Model.
I. INTRODUCTION
ireless Sensor Networks (WSNs) have become a
popular topic among researchers as it incorporates new
technologies and perspectives. Therefore, it is crucial to pay
attention to this type of network for exploring solutions to
many problems. A WSN is composed of a huge number of
sensors that are capable of observing the environment.
However, due to limited computational power and energy of
these sensors, we need to find ways to save energy. Data
fusion is a great way that saves energy as it eliminates
redundant and inaccurate data which are collected by
malicious or failed sensors. Data fusion can be defined as the
combination process of sensed data, where the resulted data
are more accurate than each one individually [1]. Data fusion
has been applied in many applications such as robotics and
military applications [2], Denial of Service (DoS) detection
[3], and sensor nodes’ locations [4].
Due to the highly beneficial use of data fusion in WSNs, this
paper provides detailed information about various data fusion
models. Our goal is to distinguish each model and provide a
Manuscript received February 6, 2014.
M. M. Almasri is a Ph.D. candidate in the Computer Science &
Engineering Department at the University of Bridgeport, Bridgeport, CT,
06604. Phone: (203) 576-4703; e-mail: maalmasr@my.bridgeport.edu.
K. M. Elleithy is the Associate Dean for Graduate Studies in the School of
Engineering at the University of Bridgeport, Bridgeport, CT, 06604. Phone:
(203) 576-4703; e-mail: elleithy@bridgeport.edu.
comparison between them. This paper also presents the
advantages and the disadvantages of each model to understand
the different objectives for applying such models.
This paper is structured as follows: section II, presents
various data fusion models and their architectures. Section III
provides a comparison between these models in order to
distinguish the objectives of each one and evaluates them
based on their advantages and drawbacks. Finally, section IV,
concludes the paper.
II. DATA FUSION MODELS
Various data fusion models are proposed especially for the
purpose of highlighting the specification, proposal, and usage
of data fusion in WSNs [5]. These models can be categorized
into three main models; data-based model, activity-based
model, and role-based model as shown in Fig. 1. This section
presents some of these models along with their architectures
and designs.
A. The JDL Model
The JDL model is a popular model in the data fusion field.
This model was introduced by the U.S. Joint Directors of
Laboratories (JDL) and the U.S. Department of Defense
(DoD) [6]. However, it has been revised by other researchers
such as Steinberg et al [7]. The JDL model is one of the data-
based models that mainly focus on the abstraction level of the
manipulated data by a fusion system. The JDL model
composed of five major processing levels, a database
management system, and a data bus that connects all
components together. The architecture of the JDL model is
represented in Fig. 2.
Fig. 1: Data Fusion Models.
Data Fusion Models in WSNs:
Comparison and Analysis
Marwah M Almasri, and Khaled M Elleithy, Senior Member, IEEE
W
2
Fig. 2: The JDL Model
As shown in Fig. 2, sources are on the left side which
demonstrate the inputs to the system. These sources can be
local sensors, distributed sensors, or any other data either from
a database or from human input [8].
In addition, on the right side, there is the Human Computer
Interaction (HCI), which allows various kinds of human inputs
such as data requests, reports, and commands. HCI provides
multimedia methods for the purpose of interacting with human
beings. The database management system is crucial as it is
responsible for maintaining the fused data. It is also
responsible for data retrieval, compression, and queries [8].
Level 0 of the JDL model called Source Preprocessing or
Process Assignment. At this level, all data collected are
assigned to appropriate processes which result in reducing the
load in the data fusion system. Level 1 is called Object
Refinement. Level 1 is responsible for transforming all kinds
of sensors data into a consistent structure. It then assigns these
data to objects in order to use the statistical estimation
techniques in many applications. It also refines the predictions
of an object’s identity. Level 2 of the JDL model is called
Situation Refinement. The Situation Refinement process
describes the relationships between objects and events and
emphasizes relational data. It interprets sensor data analogous
to human’s interpretation. It also examines level 1 results.
Threat Refinement is referred to level 3 of the JDL model. It
provides possible future threats and alternate hypotheses about
enemies. Finally, level 4 which is called Process Refinement,
is responsible for monitoring the performance of all data
fusion processes in order to present a real time control. It also
identifies what type of data is needed what are the source
specific requirements for obtaining the set goals [8].
B. Boyd Control Loop
The Boyd Control Loop is one of the activity based models
that are based on the executing of these activities in their
correct sequence. It is also known as the Observe, Orient,
Decide, Act (OODA) Loop. The Boyd Control Loop or the
OODA Loop is a four stage cyclic model which describes the
main activities in a fusion system [9] as shown in Fig. 3.
This model was proposed for decision-support in military
system [9]. The first stage is the Observe stage which provides
sensor preprocessing and data allocation. The second stage is
the Orient stage which has all data alignment processes and
situation predictions. The third stage is the Decide stage which
is responsible for making decisions. The fourth and the last
stage is the Act stage which includes the responsive system
that executes the plan [10]. According to [11], the Observe
stage of the OODA model corresponds to level 0 of the JDL
model, where the Orient stage corresponds to level 1, 2, and 3
of the JDL model. In addition, the Decide stage corresponds to
level 4 of JDL model. However, the Act stage is not included
in the JDL model.
Fig. 3, The OODA model.
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C. Intelligence Cycle
The Intelligence Cycle is an activity based model that has a
four stage cycle which describes the intelligence process for
making decisions [12]. The four stages are as follows:
Collection, Collation, Evaluation, and Dissemination as
represented in Fig. 4. Collection stage collects raw intelligence
data. Collation is responsible for collating reports to be ready
for the next stage. At the Evaluation stage, the collated reports
are fused and then analyzed, where at the Dissemination stage,
users can use the results from previous stage in order to make
decisions [13].
D. Omnibus Model
The Omnibus model is an activity based model. It was first
proposed by [11]. The Omnibus model is shown in Fig. 5
which composed of four main stages. Sensing and Signal
Processing stage is responsible for collecting and
preprocessing data. At the Feature Extraction stage, patterns
are extracted from the gathered data. After pattern extraction is
complete, these patterns are further fused. At the Decision
stage, decisions are made and threats are tracked, where the
best plan is chosen and executed at the Act stage [14].
Fig. 4: The Intelligence Cycle Model.
Fig. 5: The Omnibus Model.
E. Object-Oriented Model
The Object-Oriented model is a role based model that has a
cyclic architecture based on roles. This model was proposed
by Kokar et. al. [15]. Fig. 6 gives an overview of the Object-
Oriented model. These roles as follows: the Actor has the role
of interacting with the world, the Perceiver has the role of
evaluating and analyzing the collected data, the Director has
the role of creating plans based on the system set goals, and
finally the Manger has the role of performing the set plans by
the director [14].
III. COMPARISON AND ANALYSIS OF DATA FUSION MODELS
As shown in Table I, there are several factors to compare
and contrast the data fusion models, where it relates different
stages from each model to comprehend the objective of every
process within the data fusion domain. These factors as
follows:
Data Gathering: this explains how the data will be
collected and at which stage.
Signal Processing: this means the preprocessing of data
and data allocation before the fusion process begins.
Object Assessment: after preprocessing the data, and
patterns and features are extracted, these data are assigned to
objects.
Situation Assessment: it includes the situation
predictions, the data fusion process, and the analysis of results.
Threat Assessment: it indicates the possible threats.
Fig. 6: The Object-Oriented Model.
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Table I, comparison between data fusion models
Activity
Data
Fusion
Model
Signal
Processing
Object
Assessment
Situation
Assessment
Threat
Assessment
Decision
Making
Action
Implementation
JDL Model
Level 0
Level 1
Level 2
Level 3
Level 4
------
OODA Model
Observe
Observe
Orient
Orient
Orient
Decide
Act
Intelligence
Cycle Model
Collation
Collation
Evaluation
Evaluation
Dissemination
Dissemination
Omnibus Model
Sensing and Signal
Processing
Sensing and
Signal
Processing
Feature
Extraction
Feature
Extraction
Decision Stage
Decision Stage
Act Stage
Object-Oriented
Model
Actor
Perceiver
Perceiver
Director
Director
Manger
Decision Making: it states at which stage the decision
making process is involved either by the users or by the
system. It also provides the best plan to be implemented at the
following stage.
Action Implementation: this explains the actual plan
execution.
As indicated in Table I, the data gathering process is
applicable at all models except at the JDL model. The JDL
model starts with the Source Preprocessing stage (Level 0),
which assumes that the data is already collected and now it is
ready for the preprocessing phase. The data gathering or
data/sensor collection corresponds to Observe stage of the
OODA model, to Collection stage of the Intelligence Cycle
Model, to Sensing stage of the Omnibus Model, and finally to
Actor role of the Object-Oriented Model. For signal
processing, all models will preprocess data at early stages.
Most of the models gather data and process them at the same
stage except for the JDL model and the Intelligence Cycle
Model, where they do separate these processes at two different
stages. Object Assessment is done at Level 1of the JDL model,
Orient stage of the OODA model, Collation stage of the
Intelligence Cycle Model, Feature Extraction of the Omnibus
model, and at the Perceiver role of the Object-Oriented Model.
Situation Assessment process is done at most models as the
Object Assessment is taking place such as the OODA Model,
the Omnibus Model, and the Object-Oriented Model. In
contrast, the Situation Assessment is applied at different stages
from the Object Assessment in the JDL model (level 2), and
the Intelligence Cycle Model (Evaluation stage).
In addition, Threat Assessment is implemented sometimes
as an evaluation process after data is fused, or as a decision
needs to be made. It is done at Level 3 of the JDL model, the
Orient stage of the OODA model, at Evaluation of the
Intelligence Cycle Model, at Decision Stage of the Omnibus
model, and at the Director role of the Object-Oriented Model.
Decision Making and action implementation are done
mostly at different stages where both take place at the end of
each model. For the JDL model, the action implementation is
not applicable. However, at the Intelligence Cycle Model , the
action implementation and decision making process are done
at the same stage which is the Dissemination stage.
As a result, we can see how different stages from different
data fusion models are overlapped as the process or activity
develops from sensing and collecting data to implementing
actions and executing commands. This emphasizes the general
and the specific objectives of all fusion models. Some models
have more general goals such as the JDL model, where it
focuses on the fusion process more than acquiring data and
implementing commands. Others are more like in depth
process such as the Omnibus model. It has all components
needed for an effective data fusion model as it still has the
cyclic loop like other models but it considers the importance of
having a feedback explicitly in the system.
Furthermore, Table II, concludes and summarizes the
advantages and the disadvantages of the data fusion models
discussed in this paper. For using any data fusion model, it is
important to keep in mind the purpose of using such a model in
terms of requirements and future use. Some models are
difficult to be reused at different applications such as the JDL
Model and the Omnibus Model due to its sophisticated
techniques used or due to its adjustments to specific
5
Table II, summarizes the advantages and the limitations of the data fusion models.
Data Fusin Model
Advantages
Limitations
JDL Model
Popular and general data fusion
model.
Difficult to reuse the model after applying it at
specific application as it does not specify the
techniques used.
OODA Model
It has the ability to separate the
system tasks clearly and gives a
feedback.
It does not show the effect of the Act stage at other
stages of the model.
Intelligence Cycle
Model
General data fusion model.
Does not separate the system tasks.
Omnibus Model
It explicitly describes the
processing levels in the cyclic
loop.
If there is a specific requirement needed for an
application, this model is not the best data fusion
model as it combines several phases such as soft and
hard decisions which lead to a confusion.
Object-Oriented
Model
Describes the various roles of the
system.
Does not separate the system tasks.
application requirements which makes it complex to reapply.
Other data fusion models are more general such as the OODA
Model and the Intelligence Cycle Model.
IV. CONCLUSION
Data fusion has a vital role in WSNs as it saves the energy
consumed within the network due to its ability of eliminating
redundant data and thus increasing the accuracy and the
efficiency of the sensed data. In this paper, we investigate
different data fusion models and provide a comparison
between them based on several factors. We also highlight the
benefits and the drawbacks of each data fusion model
presented in this paper in order to understand the objective of
applying each model.
REFERENCES
[1] W. Elmenreich, “Sensor Fusion in Time-Triggered Systems”, PhD
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Mrs. Marwah M Almasri: is a Ph.D.
candidate in the Computer Science &
Engineering Department at the
University of Bridgeport. She received
award from UPE for her academic
accomplishments in the fields of the
computing and information disciplines.
She received her MBA in Management
Information System (MIS) from the
University of Scranton, PA, in 2011.
She also received award from MIS
department at the University of
Scranton for her outstanding work.
She holds a bachelor degree in Computer Science & Engineering from
Taibah University in Medina, Saudi Arabia. Her research interests include
congestion mechanisms, wireless sensor networks, computer networks,
mobile computing, network security, and data fusion.
6
Dr. Khaled M Elleithy is the Associate
Dean for Graduate Studies in the School
of Engineering at the University of
Bridgeport. He has research interests are
in the areas of network security, mobile
communications, and formal approaches
for design and verification. He has
published more than two hundreds
research papers in international journals
and conferences in his areas of expertise.
Dr. Elleithy is the co-chair of the International Joint Conferences on
Computer, Information, and Systems Sciences, and Engineering (CISSE).
CISSE is the first Engineering/Computing and Systems Research E-
Conference in the world to be completely conducted online in real-time via
the internet and was successfully running for six years. Dr. Elleithy is the
editor or co-editor of 12 books published by Springer for advances on
Innovations and Advanced Techniques in Systems, Computing Sciences and
Software.
.
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1. Abstract The Data Fusion Model maintained by the JDL Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise and expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi-sensor/multi-source systems. Data fusion involves combining information – in the broadest sense – to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people (or any other sentient beings), it can be useful to include consideration of informational and perceptual states in addition to the physical state. Developing cost-effective multi-source information systems requires a standard method for specifying data fusion processing and control functions, interfaces, and associated data bases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology. There is a general lack of standardized — or even well-documented — performance evaluation, system engineering methodologies, architecture paradigms, or multi-spectral models of targets and collection systems. In short, current developments do not lend themselves to objective evaluation, comparison or re-use.