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Range-Based and Range-Free Localization Schemes for Sensor Networks. Localization. Critical service A sensor reading consists of <time, location, measurement> E.g., target tracking, disaster recovery, fire detection, patient location in a smart hospital, … Needed for geographic routing

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localization
Localization
  • Critical service
    • A sensor reading consists of <time, location, measurement>
    • E.g., target tracking, disaster recovery, fire detection, patient location in a smart hospital, …
    • Needed for geographic routing
  • Too expensive for an individual sensor to have a GPS (Global Positioning System)
    • Reference nodes (called anchor or beacon nodes) + sensor nodes
range based localization schemes
Range-based localization schemes
  • TOA (Time of Arrival)
    • Get range info via signal propagation delay
    • E.g., GPS
    • Expensive, power consuming, inaccurate
  • TDOA (Time Difference of Arrival)
    • Transmit both radio and ultrasonic signals at the same time to observe the arrival time difference
    • Extra hardware, i.e., ultrasonic channel, is required
    • Not only radio but also sound signals have multipath effects affected by humidity, temperature, …
slide4
Received signal strength (RSS)
    • Distance estimation based on RSS
    • Hard due to radio signal vagaries
  • AoA (Angle of Arrival)
    • A node estimates the relative angles between neighbors
    • Requires directional antennae
range free localization
Range-free localization
  • Centroid algorithm
    • Anchors beacon their positions to neighbors (single hop broadcast)
    • A sensor node computes the centroid using all received beacon messages
slide6
DV-HOP
    • Anchor locations are flooded through the network
    • Keep the running hop count
    • Estimate average one hop distance
  • Amorphous Positioning
    • Similar to DV-HOP
    • Use offline one hop distance estimation
slide7

Range-Free Localization Schmes for Large Scale Sensor NEtworks- APIT (Approximate Point In Triangulation)

Mobicom 2003

pit point in triangulation
PIT (Point In Triangulation)
  • A node chooses three anchors from all audible anchors
  • Test whether it’s inside the triangle
  • Repeat for all possible combinations of audible three anchors
  • Compute the COG of the intersection of all the triangles
perfect pit test
Perfect PIT test
  • For three given anchors, A, B, C, determine whether a point M with an unknown position is inside the triangle ABC or not
  • Proposition I: If M is inside the triangle, when M is shifted, the new position is nearer to (or farther from) at least one anchor A, B, or C
slide10
Proposition II: If M is outside the triangle, when M is shifted, there must exist a direction in which the position of M is farther from or closer to all three anchors A, B and C
problems with perfect pit test
Problems with Perfect PIT test
  • How can a sensor node perform the PIT test w/o actually moving?
  • How to do exhaustive tests considering all possible directions of departure?
apit approximate pit test
APIT (Approximate PIT test)
  • In a certain propagation direction, the received signal strength is assumed to monotonically decrease in an environment w/o obstacles
  • Departure test
apit test
APIT test
  • Basic idea: Use neighbor info, exchanged via beaconing, to emulate the node movement in the perfect PIT test
  • If no neighbor of M is farther from/closer to all three anchors A, B & C simultaneously, M assumes that it is inside the triangle.
errors in the apit test
Errors in the APIT test

OutToIn Error

InToOut Error

apit error measurements
APIT error measurements

14% error when a node has 6 one-hop neighbors in average

– Small?

apit aggregation mask errors in individual apit tests
Aggregate individual APIT test results through a grid SCAN

Length of a grid side is 0.1R

For each inside decision, the values of the grid regions over which the triangle resides are incremented

Decrement for each outside decision

Find the area with max values

Take the center of gravity for position estimation

APIT aggregation: Mask errors in individual APIT tests
apit algorithm
APIT algorithm
  • 1. Each node maintains a table of anchor ID, location & signal strength
slide20
3. Run the PIT test for each column of the table
  • 4. Repeat step 3 for varying combinations of three anchors
  • 5. Use the APIT aggregation alg. to determine the area w/ max overlap
  • 6. Final location estimation = COG of that area
performance evaluation
Performance evaluation
  • Radio model
    • Upper & lower bounds on signal strength
    • Beyond the UB, all nodes are out of communication range
    • Within the LB, every node is within the comm. range
    • Between LB & UB, there is (1) symmetric communication, (2) unidirectional comm., or (3) no comm.
    • Degree of irregularity (DOI)
simulation parameters
Simulation parameters
  • Node density (ND)
  • Anchors heard (AH)
  • Anchor to node range ratio (ANR)
    • Avrg distance an anchor beacon travels/avrg distance a regular node signal travels
  • Anchor percentage (AP)
  • DOI
  • GPS error
  • Placement: uniform or random
localization error for varying ah
Localization error for varying AH
  • APIT works better as AH increases.
    • Large errors when AH < 8
  • It’s relatively less sensitive to random deployment.
localization error for varying nd
Localization error for varying ND
  • Amorphous has large errors when ND < 10
  • APIT & DV-Hop show good perf if ND >= 6
  • Amorphous is more sensitive to larger DOI
localization error for varying anr
Localization error for varying ANR
  • Error increases as ANR increases due to error accumulations
  • APIT has large errors when ANR < 3 due to large InToOut error
localization error for varying doi
Localization error for varying DOI

Irregular hop count distribution in Amorphous & DV-Hop

communication overhead for varied ah
Communication overhead for varied AH

Amorphous & DV-Hop rely on the flooding of anchor beacons

summary31
Summary
  • APIT is resilient to irregular radio patterns and random deployment
  • Relatively low overhead compared to DV-Hop & Amorphous localization (but more overhead than Centroid)
  • Localization has been well studied but still needs more work
location verification serloc secure range independent localization

Location verification – SerLoc (Secure Range-independent localization)

Workshop on Wireless Security (WiSe) 2004

what is location verification
What is location verification?
  • Different assumptions from general localization
    • What if some malicious nodes lie about their lcoation?
    • Sample attack scenario
      • Cliam to be very close to the sink
      • Attract many packets
      • Drop some or all of them
      • Very easy DoS attack especially for geographic routing protocols
how serloc works
How SerLoc works
  • Node i claims its location is (x, y)
  • Node i needs to send (x, y) a location verification request msg to a nearby verifier
    • A verifier can be a normal sensor node
  • The verifier sends a random nonce to node i and start the clock
  • Node i has to immediately return the challenge through both radio and ultrasonic channels
  • The verifier measures the time for node i returning the challenge and take the difference between the radio & ultrasonic signal propagation. Based on this observation, verify the claimed location
weakness of serloc
Weakness of SerLoc
  • Requires extra hardware, i.e., ultrasonic channel
  • Innocent victims may respond late due to backlog
  • Not location verification but range verification

sink

M’s

claimed

Location

Verifier

Oops... Verifier cannot tell

the difference! Big trouble...

M’s Real

Location

possible research issues
Possible Research Issues
  • Most localization work is mathematical and evaluated via (high level) simulations
    • More realistic work is needed
  • Indoor localization is harder
    • Look at CodeBlue project at Harvard
  • Location verification
    • Can’t trust sensors
  • Secure localization
    • Can’t trust anchors