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MoteTrack: A Robust, Decentralized Approach to RF Based Location Tracking
Paper PresentationCSE: 535 – mobile computingWeijia ChePhd student, CSE Dept, ASU
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Paper Selection
Title: MoteTrack: A Robust, Decentralized Approach to RFBased Location Tracking
Authors: Konnrad Lorincz and matt Welsh
Published: tech. report TR-19-04, Division of Eng. and Applied Sciences, Harvard Univ., 2004.
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Agenda
Motivation Scenario Background and Related Work MoteTrack Overview Robust Design Implementation Evaluation Novelty and Drawbacks Relationship with our Project References
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Motivation Scenario
Firefighters entering a large building
Heavy smoke coverage No priori notion of building layout
Indications:
Centralized approaches not suitable (central server/user’s roaming node may be destroyed)
Approaches require whole-network wireless connectivity not suitable (large num of wireless access points may have failed)
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Background and Related Work
Indoor Localization based on different context
Infrared
Ultrasound
RF-RSSI
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Indoor Localization based on Infrared
Eg. Active Badge [1]
Advantage suitable for both indoor and outdoor use
Disadvantage Many receiver nodes are required due to short range of infra
red signals Require line-of-sight exposure Suffer errors in the presence of strong light
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Indoor Localization based on ultrasound
Eg. Cricket [2,3] and Active Bat [4]
Advantage Higher accuracy
Disadvantage Requires of accurate synchronization of the sensor nodes Requires line-of-sight exposure Requires careful orientation of the receivers
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Indoor Localization based on RF
Eg. RADAR[5]
Advantage No additional hardware is required except for the sensor nodes Low power, inexpensive, easy to deploy
Disadvantage Signal strength are generally unstable Vary over time Affected by other factors (building structure, people moving around
…
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RF Indoor localization -triangulation
Model signal propagation together with current RSSI to triangulate the position of a sensor node
advantage No requirement of pre-setup database
disadvantage Requires detailed models of RF propagation Does not account for variations in receiver sensitivity and o
rientation
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RF Indoor localization -fingerprinting
Use empirical measurements of RSSI to set up a database and together with current RSSI to estimate the position of a sensor node
advantage No need for detailed models of RF propagation
disadvantage An offline calibration to set up the database is
required
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MoteTrack Overview
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Two Phases of Estimate
Offline collection of reference signatures
Reference signature format?
Online location estimation
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Online location estimation
Estimation steps I, Compute the signature distances
II, Option 1, take the centroid of the geographic location of the k nearest reference signatures (weighting with the signature distances).
II, Take the centroid of the geographic location of the nearest reference within some ratio(weighting with the signature distances).
(NOTE:C is constant, gained from experiment1.1~1.2 works well)
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Robust Design
Definition of robustness
Graceful degradation in location accuracy as base stations fail
Resiliency to information loss (poor antenna orientation)
Work well with perturbations in RF (people moving around, movement of furniture, opening or closing of doors, solar radiation …)
No single point of failure (no central server)
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Robust Design
Challenges
For decentralization consideration, beacon nodes should perform localization estimation, which leads to questions about the required resources and cost of the base stations to be answered.
In order for the technique to be resilient to loss of information, the system should be able to detect beacon failure and able to handle it
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Robust Design
Methodology
Decentralized location estimation protocol GOAL: compute the mobile node’s location in a way that only rel
ies upon local communication and at the same time to achieve low communication overhead.
Distributing the reference signature database to beacon nodes GOAL: ensure balanced distribution of reference signatures (impr
ove robustness) while attempting to assign reference signatures to their closest beacon nodes (guarantee accuracy)
Adaptive signature distance metric GOAL: handle beacon failures
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Decentralized location estimation protocol
TRY_1: k beacon nodes send their reference signature slice1. mobile node acquires its signature s by listening to beacon
nodes2. mobile node broadcasts a request for reference signatures
and gathers the slices of the reference database from k nearby beacon nodes
3. The mobile node then computes its location using the received reference signatures
Advantage very accurateDisadvantage requires a great deal of communication overhead
Alternative: contacting n<k nearby beacon nodes and ask each one only send m reference signatures that are closest to s
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Decentralized location estimation protocol
TRY_2: k beacon nodes send their location estimate1. mobile node acquires its signature s by listening to beaco
n nodes2. mobile node broadcasts its signature s to k nearby beaco
ns3. the beacon node then computes the mobile node’s loca
tion estimate and sends it back4. mobile node receives K estimate and compute the final e
stimate with these values (“centroid of centroids”)Advantage less communication overheadDisadvantage does not produce accurate location estimates
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Decentralized location estimation protocol
FINAL-SOLUTION: Max-RSSI beacon node sends its location estimate
1. mobile node acquires its signature s by listening to beacon nodes
2. mobile node broadcasts its signature s to the beason with the strongest RSSI
3. the beacon node computes the mobile node’s location estimate and sends it back
Advantage less communication overhead as long as the beacon stores an appropriate slice of refer
ence signature database, this should produce very accurate results
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Decentralized location estimation protocol
FINAL-SOLUTION: Max-RSSI beacon node sends its location estimate
1. mobile node acquires its signature s by listening to beacon nodes
2. mobile node broadcasts its signature s to the beason with the strongest RSSI
3. the beacon node computes the mobile node’s location estimate and sends it back
Advantage less communication overhead as long as the beacon stores an appropriate slice of refer
ence signature database, this should produce very accurate results
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Distributing the reference signature database to beacon nodes
Greedy distribution algorithm maxRefSigs specifies the maximum signatures each beacon node
will store For each reference signature, the beacon accepts and stores it if:
The current reference signature num is less than maxRefSigs The new reference signature contains a higher RSSI (average) val
ue than one of the stored signatureAdvantage: simplicity and no requirement for global knowledge or coordinatio
n between nodesDisvantage: some reference signatures may be stored many times with some ot
her not stored at all
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Adaptive signature distance metric
Greedy distribution algorithm Always stores the reference signature with the strongest RSSI to t
he beacon node.
Advantage: simplicity and no requirement for global knowledge or coordinatio
n between nodesDisvantage: some reference signatures may be stored many times with some ot
her not stored at all
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Distributing the reference signature database to beacon nodes
Balanced distribution algorithm Variant of a stable marriage algorithm
refer to “algorithm design” Jon Kleinberg for details
Advantage ensure balanced distribution of reference signatures while
attempting to assign reference signatures to their closest beacon nodes
Disadvantage requires global knowledge of all reference signature and
beacon node pairings individually update of beacon nodes is impossible
Note: both of those two algorithms are implemented and examined in this paper
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Adaptive signature distance metric
Bidirectional signature distance metric
( )t s r Indicates mobile node’s signature is taken at a different place rather than place of reference node r
( )t r s Indicates either mobile node’s signature is taken at a different place or beacon nodes failure
Note: Bidirectional signature distance metric put a penalty on both distance and nodes failure. is gained from experiments 0.95~1.0
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Adaptive signature distance metric
Unidirectional signature distance metric
Note: unidirectional signature distance metric only penalizes distance
Eg.
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Adaptive signature distance metric
Scheme: dynamically switches between the unidirectional and bidirectional metrics based on the fraction of local beacon nodes failure.
When few beacon nodes fail, bidirectional distance metric achieves greater accuracy
When a lot beacon nodes fail, unidirectional distance metric achieves greater accuracy (only operational nodes are considered)
Beacon nodes failure are determined dynamically by beacons periodically measure their local neighborhood.
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Adaptive signature distance metric
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Implementation
MoteTrack is implemented on the Mica2 mote platform using TinyOS operating system
20 beacon nodes are deployed at Hard University’s CS building measuring 1742 m2, with 412 m2 hallway area and 1330 m2 in room area.
482 reference signatures are measured, each with 7 power levels
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Implementation
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Evaluation
Location estimation protocols
Employedprotocol; MaintainsSimilar Accuracy While Achieve Very lowCommunicationoverhead
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Evaluation
Selection of reference signatures
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Evaluation
Distribution of the reference signature database
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Evaluation
Transmission of beacons at multiple power levels
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Evaluation
Density of beacon nodes
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Evaluation
Density of reference signatures
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Evaluation
Robustness to perturbed signatures
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Evaluation
Time of day and different motes
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Evaluation
Hallways, rooms, and door position
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Evaluation
Robustness to beacon node failure
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Novelty
Decentralized location estimation protocol
Distribution of partial reference signature database to beacon nodes
Dynamic adapt to nodes failure through employing different distance metric
Employ multiple power levels
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Drawbacks
The beacons have to be installed and the database be set up before the scheme could be used
Tricky Point: the system actually employs more beacons than needed to achieve the same accuracy and also stores redundancy information
However, this enables it to handle with beacon nodes failure and achieve robustness
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Relationship with our Project
Our Project Proposed In the Paper
Environment basically stable:Accuracy is the first consideration
Environment highly volatile:
Robustness is the first
consideration Deployment in a small area:Typically a roomOnly a small amount beacons will be used
Deploy in a large area: One whole floor 20 beacon nodes are used
Computation using centralized server
Computing within beacon nodes and is decentralized
RF tag will be small with the most basic functions and merely no computation
RF tag do a small amount of computation, eg. searching for the beacon nodes with the strongest RSSI
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References
[1] A. Smailagic, J. Small, and D. P. Siewiorek. “Determining User Location For Context Aware Computing Through the Use of a Wireless LAN infrastructure.” December 2000.
[2]N. B. Priyantha, A. Miu, H. Balakrishnan, and S. Teller. “The Cricket Compass for Context-Aware Mobile Applications.” In Proc. 7th ACM MobiCom, July 2001.
[3] S. Ray, D. Starobinski, A. Trachtenberg, and R. Ungrangsi. “Robust Location Detection with Sensor Networks.” IEEE JSAC, 22(6), August 2004.
[4] G. Slack. “Smart Helmets Could Bring Firefighters Back Alive.” FOREFRONT, 2003. Engineering Public Affairs Office, Berkeley.
[5] P. Bahl and V. Padmanabhan, "RADAR: An In-Building RF-Based User Location and Tracking System,“ Proc. IEEE Infocom 2000, IEEE CS Press
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Appendix Pseudo code for greedy algorithm
foreach (BN in allBNs) { foreach (refSig in allRefSigs) { if (BN.size < maxNbrRefSigs) BN.assign(refSig) else if (refSig.RSSIValFromBN(BN) > BN.minR
SSI) BN.remove(BN.minRSSI) BN.assign(refSig) } }
Pseudo code for balanced algorithm Invariants ---------- (1) no refSig is assigned more than one additional time from any other refSig (i.e., every refSig has to be assigned at least once before a refSig can be assigned a second time) (2) no BN is assigned a refSig more than one additional time from any other BN
Algorithm --------- L <= construct a list of all <BN,refSig> pairs and sort them by distance between BN and refSig while (there are more elements to assign) { if (possible to assign the next pair from L such that no invariant is violated) make assignment else { // resolve deadlock b <= next BN from L that has been assigned a refSig the least number of times r <= next refSig from L that has been assigned to a BN the least number of times pair <b,r> // note: this violates an invariant while (an invariant is violated) // backtrack swap r with the previously assigned refSig }
}
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End
Thanks !
Questions?