MoteTrack: A Robust, Decentralized Approach to RF Based Location Tracking

Paper PresentationCSE: 535 – mobile computingWeijia ChePhd student, CSE Dept, ASU

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.

Agenda

Motivation Scenario Background and Related Work MoteTrack Overview Robust Design Implementation Evaluation Novelty and Drawbacks Relationship with our Project References

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)

Background and Related Work

Indoor Localization based on different context

Infrared

Ultrasound

RF-RSSI

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

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

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

…

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

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

MoteTrack Overview

Two Phases of Estimate

Offline collection of reference signatures

Reference signature format?

Online location estimation

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)

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)

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

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

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

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

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

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

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

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

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

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

Adaptive signature distance metric

Unidirectional signature distance metric

Note: unidirectional signature distance metric only penalizes distance

Eg.

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.

Adaptive signature distance metric

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

Implementation

Evaluation

Location estimation protocols

Employedprotocol; MaintainsSimilar Accuracy While Achieve Very lowCommunicationoverhead

Evaluation

Selection of reference signatures

Evaluation

Distribution of the reference signature database

Evaluation

Transmission of beacons at multiple power levels

Evaluation

Density of beacon nodes

Evaluation

Density of reference signatures

Evaluation

Robustness to perturbed signatures

Evaluation

Time of day and different motes

Evaluation

Hallways, rooms, and door position

Evaluation

Robustness to beacon node failure

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

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

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

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

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 }

}

End

Thanks !

Questions?