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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. Introduction. Motivation. System model. Issues. Mobile Localization Algorithm.

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a dynamic and reliable location tracking approach for mobile environments

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.

outline
Introduction.

Motivation.

System model.

Issues.

Mobile Localization Algorithm.

Implementation.

Results.

Comparison.

Conclusion and future work.

Outline .
localization
Localization.

What is Localization?

A mechanism for discovering spatial relation ship between objects.

Requirements:

  • Reliable.
  • Accurate.
  • Cost efficient.
types of localization tagged
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.
motivation
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.

system model
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.
issues
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.
mobile localization
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

mobile localization continued
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)

implementation
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.

implementation idea
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.
acoustic ranging distance error
Acoustic Ranging - Distance Error.

1% linear distance error till 10m.

simulation
Simulation.
  • A GUI to simulate mobile environment.
  • Implementation Data is incorporated.
    • 1% linear distance error till 10m.
    • 5% packet loss error.
results euclidian error plot
Results- Euclidian error plot.

A maximum error of 5.2 cm.

results cumulative plot
Results- Cumulative plot.

Error linear all through node path.

performance wrt time interval for localization
Performance wrt Time Interval for localization.

Error is directly proportional to time interval for location refresh.

performance wrt radio range
Performance wrt Radio Range.

Error is inversely proportional to Range of Radio units.

performance wrt node mobility
Performance wrt Node Mobility.

Error is independent of node mobility.

performance wrt number of mobile nodes
Performance wrt number of Mobile nodes.

Error is inversely proportional to number of mobile nodes.

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.

Comparison.
conclusion
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.
future work
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.
references
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”, Int’l 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.