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Range-Based and Range-Free Localization Schemes for Sensor Networks
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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
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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, …
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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
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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
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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
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Range-Free Localization Schmes for Large Scale Sensor NEtworks- APIT (Approximate Point In Triangulation)
Mobicom 2003
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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
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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
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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
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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?
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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
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Signal strength at different distances (to justify the departure test)
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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.
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Errors in the APIT test
InToOut Error OutToIn Error
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APIT error measurements
14% error when a node has 6 one-hop neighbors in average – Small?
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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
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APIT algorithm
1. Each node maintains a table of anchor ID, location & signal strength
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2. Nodes exchange anchor tables with the neighbors
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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
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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)
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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
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Localization error for varying AH
• APIT works better as AH increases. • Large errors when AH < 8
• It’s relatively less sensitive to random deployment.
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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
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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
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Localization error for varying DOI
Irregular hop count distribution in Amorphous & DV-Hop
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Communication overhead for varied AH
Amorphous & DV-Hop rely on the flooding of anchor beacons
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Communication overhead for varied ND
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Summary
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Localization error impact on geographic forwarding
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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
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Location verification – SerLoc (Secure Range-independent localization)
Workshop on Wireless Security (WiSe) 2004
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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
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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
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Weakness of SerLoc Requires extra hardware, i.e., ultrasonic channel Innocent victims may respond late due to backlog Not location verification but range verification
Verifier
M’s RealLocation
M’s claimedLocation
sink
Oops... Verifier cannot tellthe difference! Big trouble...
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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
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Questions?