A worth reading presentation on Data Fusion

1. A New Approach to Data Fusion
in
UnderWater Wireless Sensor Networks
YOUSEF EMAMI
yousef.emami@ieee.org
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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2. Agenda
Agenda
Rudiments
Applications
Challenges
Algorithms
Literature Review
Conclusion
Reference
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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3. IEEE Remote Sensing Society Data Fusion Technical Committee defines
data fusion as below:
The process of combining data provided by different instruments and source in order to
improve the processing and interpretation of these data.
Hall defines data fusion as below:
A combination of data from multiple sensor to accomplish improved accuracy and more
specific inferences that could achieved by the use of single sensor alone
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
s
Literature
Review
Reference
Conclusio
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4. Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
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Literature
Review
Reference
Conclusio
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The relationship among Fusion Terms

5. Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
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Literature
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Conclusio
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Data Fusion in the Information-Processing Cycle

6. Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
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Literature
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Conclusio
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Data Mining in the Information-Processing Cycle

7. Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
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Classification Based on Relationship among Resources

8. Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
s
Literature
Review Reference
Conclusio
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9. The major challenges in the design of underwater sensor networks are as follows:
 The available bandwidth is severely limited
 Propagation delay under water is five orders of magnitude higher than that in RF
terrestrial channels and extremely variable
 High bit error rates and temporary losses of connectivity
 Battery power is limited and usually batteries cannot be recharged; also, solar energy
cannot be exploited.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Technique
s
Literature
Review Reference
Conclusio
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10. Terrestrial Sensor Networks vs. Underwater Sensor Networks
 Size and cost
 Deployment
 Power
 Memory
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Data Fusion
UWSNs
Applications Challenges
Algorithms
Methods
Techniques
Literature
Review
Reference
Conclusio
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11.  Robotics
 Intelligent Transportation Systems Precision Agriculture
 Security, Improving Intrusion Detection Data Privacy
 Assessing and Monitoring Civil Infrastructures Environmental
Monitoring
 Fire Detection
 Financial analysis
 Fault diagnosis
 Medical Diagnoses
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
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12.  Improving Barrier Coverage in Wireless Sensor Networks
 Detect routing failures
 Collect link statistics for routing protocols
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
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13.  Data imperfection
Data provided by sensors is always affected by some level of impreciseness as well as
uncertainty in the measurements.
 Data modality
Sensor networks may collect the qualitatively similar (homogeneous) or different
(heterogeneous) data, Both cases must be handled by a data fusion scheme.
 Outliers and spurious data
Data fusion algorithms should be able to exploit the redundant data to alleviate such
effects
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
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14.  Processing framework
Data fusion processing can be performed in a centralized or decentralized
manner.
 Data dimensionality
The measurement data could be preprocessed, either locally at each of the sensor
nodes or globally at the fusion center to be compressed into lower dimensional
data, assuming a certain level of compression loss is allowed
 Data alignment/registration
Sensor data must be transformed from each sensor’s local frame into a common
frame before fusion occurs. Data registration is of critical importance to the
successful deployment of fusion systems in practice.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Technique
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Literature
Review
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15. Inference
 Bayesian
 Dempster-Shafer
 Fuzzy Logic
 Neural Network
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Techniques
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16. Estimation
 Least squares
 Maximum likelihood
 Moving average filter
 Kalman filter
 Particle filter
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Techniques
Literature
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17. Rough set based fusion
 A mathematic method which analyses and treats vagueness and uncertainty, and offers an effective
method to data fusion system.
 It can analyze incomplete or inaccurate data to extract useful information.
 Rough set theory is now mainly applied in decision making of distributed data fusion system.
.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Introduction Applications Challenges
Algorithms
Methods
Techniques
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18. Data fusion in intelligent transportation systems: Progress and challenges – A survey by El Faouzi(2011)
A variety of functions are assigned to ITS to address the traffic congestion and safety problems:
 Automatic incident detection (AID)
 Advanced driver assistance (ADAS)
 Network control, crash analysis and prevention
 Traffic demand estimation
 Traffic forecast and monitoring
 Accurate position estimation
Each of these sub-systems can make use of different information sources. DF techniques can then be used to
combine them to yield better results.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudimentd Applications Challenges
Algorithms
Methods
Technique
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Literature
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19. Energy efficient wireless sensor network communications based on computational
intelligent data fusion for environmental monitoring by D.F.Larios(2012)
Goal: Optimizing energy consumption in an environmental monitoring process
Method: The data fusion is based on a local Self-Organized Map(SOM)
Results: Enhancing network lifetime
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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RudimentsApplications Challenges
Algorithms
Methods
Technique
s
Literature
Review
Reference
Conclusio
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20. An approach to implement data fusion techniques in wireless sensor networks using
genetic machine learning algorithms by Pinto(2014)
Goal: Optimizing the communication efficiency in dense wireless sensor network
Method: Genetic Algorithm
Result: The proposed approach is able adjust the sending rate of a WSN
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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RudimentsApplications Challenges
Algorithms
Methods
Technique
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Literature
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21. A Power-Aware Framework for Distributed Data Fusion Application in Wireless Sensor
Networks by Zongqing Lu(2011)
Goal: Mapping distributed data fusion application into networks.
Method: Fusion function placement and fusion function transfer maintenance.
Result: PAFusion has better performances than DFuse and PAFusion has much less
overhead and causes only slightly more transmission cost than optimal solution.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Technique
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Literature
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Conclusio
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22. Multisensor data fusion for underwater navigation by Majumder(2001)
Goal: Integrating information from a number of sensors to navigate a sub-sea vehicle
Method: Data Fusion
Result: Successful Navigation
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Technique
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23. Multi-sensor Data Fusion for Underwater Target Recognition Under Uncertainty by Tang
Zheng(2010)
Goal: Under Water Target Recognition
Method: A dynamic information fusion framework which is based on discrete dynamic
bayesian network (DDBN) for representing, integrating and inferring various target
characteristics from dynamic sensory information of different modalities
Result: The experimental results demonstrate the utility of the proposed method in
efficiently modeling and inferring dynamic events.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Technique
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24. An Energy-Efficient Data Fusion Protocol for Wireless Sensor Network by Bin Zeng
(2007)
Goal: Proposing a data fusion protocol for gathering event data in sensor networks
Results: LEECF not only optimizes the transmission and fusion energy costs, but also
increases the fusion speed for sensor nodes. LEECF is completely distributed, requiring no
control information from the sink after node level is set up at first, and the nodes do not
require knowledge of the global network in order for LEECF to operate.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
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Methods
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25. Other applications:
Data fusion for underwater target tracking by S. Koteswara Rao(2009)
Data Fusion Method for Underwater Object Localization by Yun Lu (2013)
Application of the Multi-sensor Fusion Method for Underwater Landscape Modeling By
Noel(2014)
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
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26. To minimize energy consumption of Underwater wireless sensor networks ,Data fusion
techniques are good candidate and can prolong lifetime of UWSNs.
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
26
Rudiments Applications Challenges
Algorithms
Methods
Technique
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Literature
Review
ReferenceConclusion

27. THANK YOU FOR YOUR KIND ATTENTION
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments
Applications
Challenges
Algorithms
Methods
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Problem
Statemen
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28. 1. Lawrence A. Klein, “Sensor and Data Fusion” ,SPIE Press,2010
2. Ahmed Abdelgawad,Magdy Baypumi ,” Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks”, Springer
Publication, 2012
3. Martin E. Liggins ,David L. Hall,James Llinas,”Handbook of Multisensor Data Fusion”,CRC Press,2009
4. E. F. Nakamura, et al., "Information fusion for wireless sensor networks: Methods, models, and classifications," ACM Comput.
Surv., vol. 39, p. 9, 2007.
5. Ian F. Akyildiz, Mehmet Can Vuran, “Wireless Sensor Networks”, John Wiley & Sons,2010
6. B. Khaleghi, et al., "Multisensor data fusion: A review of the state-of-the-art," Information Fusion, vol. 14, pp. 28-44, 2013.
7. N.-E. E. Faouzi, et al., "Data fusion in intelligent transportation systems: Progress and challenges – A survey," Information Fusion,
vol. 12, pp. 4-10, 2011.
8. B. J. Larios D.F, Rodríguez G, "Energy efficient wireless sensor network communications based on computational intelligent data
fusion for environmental monitoring," Communication, vol. 6, pp. 2189-2197, 2012.
9 A.R.Pinto,et al.,” An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning
algorithms”, Information Fusion,vol.15,pp.90-101,2014
10. S. Koteswara Rao, K.S. Linga Murthy, K. Raja Rajeswari.” Data fusion for underwater target tracking “,vol.4,pp.576-585,2010
Department of Computer Engineering and Information
Technology-Shiraz University of Technology
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Rudiments Applications Challenges
Algorithms
Methods
Technique
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Literature
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