A Dynamic and Reliable Location Tracking Approach for Mobile Environments.

1
A Dynamic and Reliable Location Tracking Approach
for Mobile Environments.

• Masters Thesis Defense of
• Pavan K. Nallamothu.
• Advising Committee
• Dr. Sandeep Gupta.
• Dr. Chaitali Chakrabarthi.
• Dr. Karam Chatha.

2
Outline .

• Introduction.
• Motivation.
• System model.
• Issues.
• Mobile Localization Algorithm.
• Implementation.
• Results.
• Comparison.
• Conclusion and future work.

3
Localization.

• What is Localization?
• A mechanism for discovering spatial relation
  ship between objects.
• Requirements
• Reliable.
• Accurate.
• Cost efficient.

4
Types of localization. (Tagged)

• Active Localization
• System sends signals to localize targets.
• GPS, Cricket.
• Passive Localization
• System deduces localization of targets from
  signals.
• Bat, AHLoc.
• Disadvantages
• Depends of fixed infrastructure.
• Not defined for mobile environment.

5
Motivation.

• Motivation
• Need for localization in mobile environment,
  which is Ad-hoc, Reliable and Cost efficient
  simultaneously.
• Example Scenario
• Mobile nodes monitoring unfriendly terrains or
  hazardous regions.

6
System Model.

• Nodes placed in 2D environment.
• Environment has no fixed infrastructure.
• Nodes are mobile.
• Assumptions
• Nodes have wireless communication and
  computational capabilities.
• Nodes are time synchronized, have unique id.
• Nodes move at standard pace and have direction
  information.
• Nodes know their initial location.

7
Issues.

• Signal collision because multiple nodes want to
  communicate at the same time
• Time multiplex nodes.
• Vast beacon messages spread in the environment
• Beacon time out value.
• Wrong location estimate because nodes are mobile
  
• Approximate location information.
• Multi-path effect of signals because of
  reflection, scattering
• Unique Id.
• TOF between RF and Acoustic pulse.

8
Mobile Localization.

• Involves two kinds of localization
• Approximate Localization From direction of
  motion ( ), speed ( )and time interval ( )
  information.
• Actual Localization At the time multiplexed slot
  node becomes subject to updated their
  localization information.
• Subject discovers neighboring
• nodes in range.

SUB
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Mobile Localization Continued

• Subject identifies nearest neighbors as D1, D2.

SUB
D2
D1
(X, Y)

• Subject triangulates its location from D1, D2.

R2
R1
(Xd2, Yd2)
(Xd1, Yd1)
10
Implementation.

• Tinyos (UC Berkeley)- operating system for
  wireless sensor networks.
• Provides standard commands and hardware event
  interrupts generate by hardware units called
  motes.
• Motes have
• 4Mhz Atmel Processor
• 128kb onboard flash memory
• RF 916.5 MHz 10kbps
• Sounder, Mic, light sensor,
• Accelerometer.

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Implementation Idea.

• Localization with 6 nodes in static environment.
• Implemented through state diagram.
• Onboard LED configuration for state information.
• Acoustic ranging method (Vanderbilt University)
• measuring the time of flight sound signal between
  the signal source and the acoustic sensor.

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Acoustic Ranging - Distance Error.
1 linear distance error till 10m.
13
Localization- Led Configuration.
Demo
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Simulation.

• A GUI to simulate mobile environment.
• Implementation Data is incorporated.
• 1 linear distance error till 10m.
• 5 packet loss error.

15
Mobility Plot.
16
Results- Euclidian error plot.
A maximum error of 5.2 cm.
17
Results- Cumulative plot.
Error linear all through node path.
18
Result- Histogram of Mobile Localization Error.
19
Performance wrt Time Interval for localization.
Error is directly proportional to time interval
for location refresh.
20
Performance wrt Radio Range.
Error is inversely proportional to Range of Radio
units.
21
Performance wrt Node Mobility.
Error is independent of node mobility.
22
Performance wrt number of Mobile nodes.
Error is inversely proportional to number of
mobile nodes.
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Comparison.

• Mobile Location Tracking
• Does not need fixed infrastructure.
• Cost efficient.
• Effective against signal fading, packet dropping,
  Multi path effects.
• Average error 3.85cm
• Standard deviation 2.4175cm

• Mobile Agent approach
• Infrastructure dependent.
• Expensive
• Grossly effects performance.
• Average error 19.4875cm.
• Standard Deviation 27.12515cm.

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Conclusion.

• Proposed a protocol from localization in mobile
  environments.
• Reliability of the protocol is verified with
  successful implementation and simulation.
• Protocol performs better compared to Mobile agent
  approach.

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Future work.

• The protocol can be extended to cover wide range
  environments.
• Performance of the protocol can be enhanced by
  incorporating Doppler effect in calculating
  distance between mobile nodes.
• Protocol can be extended to scenario like
  navigating customers in shopping mall.

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References.

• Nissanka B.P., Chakraborty A.,, Balakrishnan H.,
  The Cricket location support system. In
  Proceedings of the Sixth International Conference
  on Mobile Computing and Networking, August 2000.
• Bahl P., Padmanabhan V. N., RADAR An
  in-building RF-based user location and tracking
  system, In Proceedings of IEEE Conference on
  Computer Communications, 2 775-784, March 2000.
• Tseng Y. C., Kuo S. P., Lee H. W., Huang C. F.,
  A mobile-agent approach for location tracking in
  a wireless sensor network, Intl Computer Symp,
  2002.
• Sallai J., Balogh J., Maroti M., Ledeczi A.,
  Acoustic Ranging in Resource Constrained Sensor
  Networks, Technical Report, 2004.
• Hightower J., Borriello G., A Survey and
  Taxonomy of Location Systems for Ubiquitous
  Computing, Extended paper from Computer, 34(8)
  57-66, August 2001.
• Design Y., Indoor Radio WLAN Performance Part
  II Range Performance in a Dense Office
  Environment,, IEEE 802.11 Tutorials, November
  2003.

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Thank You.