MoteTrack: A Robust, Decentralized Approach to RF Based Location Tracking Paper Presentation CSE: 535 – mobile computing Weijia Che Phd 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 infrared 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 orientation

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 experiment 1.1~1.2 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 relies 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 (improve 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 slice 1. mobile node acquires its signature s by listening to beacon nodes 2. 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 accurate Disadvantage 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 estimate 1. mobile node acquires its signature s by listening to beacon nodes 2. mobile node broadcasts its signature s to k nearby beacons 3. the beacon node then computes the mobile node ’ s location estimate and sends it back 4. mobile node receives K estimate and compute the final estimate with these values ( “ centroid of centroids ” ) Advantage less communication overhead Disadvantage 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 reference 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 reference 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) value than one of the stored signature Advantage: simplicity and no requirement for global knowledge or coordination between nodes Disvantage: some reference signatures may be stored many times with some other not stored at all

Adaptive signature distance metric  Greedy distribution algorithm Always stores the reference signature with the strongest RSSI to the beacon node. Advantage: simplicity and no requirement for global knowledge or coordination between nodes Disvantage: some reference signatures may be stored many times with some other 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 Indicates mobile node ’ s signature is taken at a different place rather than place of reference node r 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 m 2, with 412 m 2 hallway area and 1330 m 2 in room area.  482 reference signatures are measured, each with 7 power levels

Implementation

Evaluation  Location estimation protocols Employed protocol; Maintains Similar Accuracy While Achieve Very low Communication overhead

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 ProjectProposed 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 room Only 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 [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 [3] S. Ray, D. Starobinski, A. Trachtenberg, and R. Ungrangsi. “Robust Location Detection with Sensor Networks.” IEEE JSAC, 22(6), August [4] G. Slack. “Smart Helmets Could Bring Firefighters Back Alive.” FOREFRONT, 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.minRSSI) 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 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 // note: this violates an invariant while (an invariant is violated) // backtrack swap r with the previously assigned refSig }  }

End Thanks ! Questions?