Wireless Sensor Networks Positioning Algorithms

1
Wireless Sensor NetworksPositioning
Algorithms Energy Management

• Sherry Adair
• Beaux Sharifi
• CS526
• Spring 2005

2
Agenda

• Motivation
• Positioning Algorithms
• Energy Management
• References

3
Example Applications
4
Example Applications (cont)
UC Berkeley Biology Research
5
Research Focus

• Positioning Algorithms
• Energy Management
• Positioning Algorithms are distributed heuristic
  algorithms used to determine the local or global
  coordinate positions of nodes in an ad-hoc
  wireless sensor network.
• Most applications implicitly require positioning
  information
• Most research topics are focused on methods for
  saving energy

6
Positioning Agenda

• Background
• 3 Different Algorithms
• Simulation Results
• Conclusion

7
Positioning Background

• 2D Trilateration

• 3D Trilateration

a
b
b
c
d
a
c
8
Positioning Background (cont)

• Two Major Difficulties to Positioning
• Sparse Anchor Node Problem
• Range Error Problem

9
Positioning Algorithms

• ABC Assumption Based Coordinate Savarese,
  Rabaey, Beutel, 2001
• TERRAIN - Triangulation via Extended Range and
  Redundant Association of Intermediate
  Nodes Savarese, 2002
• Hop-TERRAIN Savarese, 2002
• Two-Phase Savarese, 2002
• First Phase Hop-TERRAIN
• Second Phase Refinement

10
TERRAIN Algorithm
ABC Algorithm n0 (0,0) n1 (r01, 0) n2 (r012
r022 r122) , r022 x22 )
2
(6,6)
(3,5)
2r01
3
(1,3)
Anchor Node Distance



GLOBAL POSITION GLOBAL POSITION
(3,2)
(5,2)
1
sqrt(62 62)
8.5
1
(0,0)
(5,1)
2
4.3
(3,0)
3
1.2
(18, 24)
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Hop-TERRAIN Algorithm

• Binary nature provides following benefits
• No compounding of errors at each hop
• Provides consistent results
• Scales to much larger networks

2
3
Anchor Node Distance



GLOBAL POSITION GLOBAL POSITION
1
1
3 Hop Metric
6
4
2
3
2
(18, 24)
12
Two-Phase Refinement Algorithm

• First Phase Hop-TERRAIN
• Detects Edge Independence (for poor topologies)
• Second Phase Refinement
• Iterative improvement of positions via ranges
  until position converges
• Uses Confidence Metrics (for convergence)

13
Simulation ResultsTERRAIN vs. Hop-TERRAIN
Range Error Sensitivity of Hop-TERRAIN and
TERRAIN (nodes 40, anchors 4, range 10,
grid 30x30)
14
Simulation Results (cont)Hop-TERRAIN vs.
Refinement
Average Position Error After Refinement (5 Range
Errors)
Average Position Error After Hop-TERRAIN (5
Range Errors)
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Simulation Results (cont)Hop-TERRAIN vs.
Refinement
Range Error Sensitivity between Hop-TERRAIN and
Refinement (10 Anchors, 12 Nodes Connectivity)
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Positioning Conclusion
Algorithm Sparse Anchor Problem Range Error Problem
TERRAIN
Hop-TERRAIN
Two-Phase
17
Future Positioning Research

• Total Least Squares Algorithm
• Hop-Refinement

18
Energy Agenda

• Importance of Energy Management
• Sources of Wasted Energy
• Methods of Reducing Energy Consumption
• Future Research
• Conclusions

19
Importance of Energy Management

• Thousands of motes
• Not feasible to access them because of location,
  or quantity
• Reliability of application depends on motes
  continuing to operate
• Required to operate for many years

20
Source of Wasted Energy

• Transmissions
• Collisions
• Overhearing
• Control packet overhead
• Idle listening
• Lossy links

21
Methods of Reducing Energy Consumption

• Algorithms designed with power consumption in
  mind
• Special MAC protocols (S-MAC, B-MAC)
• Active/Sleep periods
• Decreasing the sensing coverage area
• Data Reduction
• Shorter, more reliable links
• Scavenging Power from solar, vibration using
  custom IC

22
Special MAC Protocols

• Needed to focus on energy management
• Based on 802.11 protocol
• Use active/sleep schedule
• Collision Avoidance
• Increase latency
• Reconfigure network based on current load
• (B-MAC)

23
Example of Energy Saved by Sleeping

• System Components
• StrongArm SA-1110 microprocessor
• Sensor
• Radio

24
Mica2 sleep savings
Full operation of the sensor requires about 15ma
of current AA batteries supply 1800 ma which
would last about 120 hours or 5 days
25
Shorter, more reliable links
26
Energy Scavenging
27
Energy Scavenging (cont)
28
Energy Scavenging
PicoRadio Meso-scale radio
29
Moores Law

• Capabilities increasing
• Costs staying the same
• Power consumption staying the same
• Reduced power consumption for special purpose
  nodes

30
Future Research

• Renewable sources of energy
• MAC protocols designed especially for WSN
• Custom low power ICs

31
Energy Conclusions

• Much energy is spent in the communication task of
  the mote, with almost as much energy required to
  listen as to send
• Special MAC protocols are required to address the
  special needs of WSN such as conserving power and
  adjusting to the changing network topology
• Active/sleep schedule is a common method used to
  conserve energy. Tradeoff is latency in packet
  delivery
• Possibility of extending the lifetime of motes
  using renewable energy sources such as solar and
  vibration

32
References

• http//bwrc.eecs.berkeley.edu/People/Faculty/jan/p
  resentations/AmbientIntelligence.pdf
• Jason Hill, Mike Horton, Ralph Kling, Lakshman
  Krishnamurthy. The Platforms Enabling Wireless
  Sensor Networks. Communications of the ACM June
  2004/ Vol47. No. 6. p 41-46.
• C. Savarese, Robust Positioning Algorithms for
  Distributed Ad-Hoc Wireless Sensor Networks,
  Masters Thesis, 2002.
• C. Savarese, J. Rabaey, and J. Beutel,
  Locationing in Distributed Ad-hoc Wireless
  Sensor Networks, in IEEE International
  Conference on Acoustics, Speech, and Signal
  Processing (ICASSP), pages 2037-2040, Salt Lake
  City, UT, May 2001
• Eugene Shih, Seong-Hwan Cho, Nathan Ickes, Rex
  Min, Amit Sinha, Alice Wang, and Anantha
  Chandraskasan. Physical Layer Driven Protocol
  and Algorithm Design for Energy-Efficient
  Wireless Sensor Networks.