Distributed Localization for Wireless Distributed Networks in Indoor Environments

Distributed Localization for Wireless DistributedNetworks in Indoor Environments

Hermie P. Mendoza

Wireless @ VTVirginia Polytechnic and State University

June 28, 2011

Masters Thesis Defense Presentation

Agenda

1 Preliminaries of PL and WDC

2 Fingerprint-based PL

3 WDC-based Fingerprinting System

4 Algorithm Performance and Results

5 PL Demo

6 Conclusion and Future Work

Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 2 / 46

Preliminaries

Preliminaries Overview

Location-Awareness in Ubiquitous Computing

Position Location Fundamentals

Wireless Distributed Computing (WDC) Fundamentals

Why Position Location and WDC?

Preliminaries Position Location

Location Awareness in Ubiquitous Computing

Figure: User accessing location-based service on a smartphone.

The Principles of Positioning I

Positioning Problem: Reasonably localize an object within aglobal or local frame of reference.

The Principles of Positioning II

Figure: Summary of Position Location

The Principles of Positioning III

Figure: Summary of Position Location

Preliminaries Wireless Distributed Computing

What is WDC?

New paradigm emphasing distributed information services!

Figure: Information service shift from centralized to de-centralized computation.

Preliminaries Benefits

Benefits of WDC

Potential Benefits Results

1 Lower energy and powerconsumption per node

2 Efficient load balancingacross collaborating nodes

3 Harnesses availablenetwork resources

4 Robust, secure, & faulttolerant execution

5 Simplifies radio’s formfactor

Extends total networklifetime

Better resource demandand supply matching

Meets computationallatency requirements ofcomplex processing tasks

Attain stringent QoSrequirements

Economic cost savings

Benefits of WDC

Preliminaries Location Awareness for WDC Paradigms

Location Awareness for WDC Paradigm

Improve overall wirelesscommunication system

Needed to achieveinteroperability

Figure: Cognitive radio sensingenvironment

Preliminaries PL and WDC

Motivations I

Localization is generally accomplished in a centralized manner at theexpense of a single network node’s resources. Can the problem ofpositioning be solved in a distributed manner or parallelized?

Figure: Resource constrained mobile phone.

Preliminaries PL and WDC

Motivations II

(a) Point inside the mall (b) Point inside an airport

Preliminaries Min Makespan

Min Makespan Problem I

Minimize the time taken to compute the individual localizationcalculations.

Problem Formulation

Given a set of J of m jobs and a set of N of n nodes, theprocessing time for a job j ∈ J on node i ∈ N is pij ∈ Z

+. Thenwe must find an assignment of the jobs J to the nodes N suchthat the makespan, or the completion time, is minimized.

Preliminaries Min Makespan

Min Makespan Problem II

Integer programming formulation

minimize t

subject to∑i∈N

xij = 1, j ∈ J

∑j∈J

xijpij ≤ t, i ∈ N

xij ∈ {0, 1} , i ∈ N, j ∈ J

Fingerprinting High Level Overview

Fingerprint Overview

Problem Formulation

The Fingerprint

Fingerprinting Algorithms

Fingerprinting Problem Formulation

Fingerprint Problem Statement

Problem Statement

Using only RSS observations of an arbitrary transmitter, locate andestimate its position in a distributed manner.

Distributed algorithms must be flexible and applicable for variousfingerprint-based positioning systems.

Computational nodes must form a WDCN.

Fingerprinting The Fingerprint

The Fingerprint I

Figure: Fingerprinting Concept

Fingerprinting The Fingerprint

The Fingerprint II

Mathematical Interpretation

(xi , yi ) = [FP1,FP2, . . . ,FPn] (2)

for fingerprint location i , using n sensor nodes.

Alternative Interpretation

f = (xi , yi ) = [FP1,FP2, . . . ,FPn] (3)

for fingerprint location i , using n sensor nodes.

Fingerprinting Fingerprinting Algorithms

Euclideandistance

Bayesianmodeling

NeuralNetworks

Deterministic positioning method

√√√√ n∑i=1

∣∣FPi − FP′i

∣∣2 (4)

(x , y) = minFPi

√√√√ n∑i=1

∣∣2 (5)

Euclideandistance

Bayesianmodeling

NeuralNetworks

Probabilistic positioning method

P( l | f ) = P ( f | l)P(l)P(f )

, P(f ) �= 0 (4)

P( f | l) =n∏

P( fj | l) (5)

P ( lt | lt−1) =

∫P(lt | l ′t−1

′t−1) dl

′t−1 (6)

(x , y) = maxl

P ( f | l)P(l) (7)

Euclideandistance

Bayesianmodeling

NeuralNetworks

Pattern Recognition

Distributed Target Localization I

Distributed Localization Approaches

Transfering computationally complex operations to a single node withgreater capabilities.

Parallelizing the position location calculations.

Distributed Target Localization II

Figure: Partitioning a service area for a WDCN.

Notations

f number of fingerprint locations

p number of partitions

(x , y) estimated position of user

gi vector of probabilities calculated by node i

FPi tuple of RSS at fingerprint location i

FP i vector of distances calculated by node i

FPi RSS received at sensor i

FP′i RSS database entry of sensor i

pi AOR or partition assigned to a node i

Distributed Euclidean Distance Algorithm (DEDA) I

Centralized Approach

(x , y) = minFPi

√√√√ f∑i=1

∣∣2 (4)

Distributed Euclidean Distance Algorithm (DEDA) II

Distributed Approach

Initialize FPi = 0.while pi is assigned and received, do

for all FP′j ∈ fj , do

FP i ←√∑j

∣∣FPk − FP′k

∣∣2end for

end while(xj , yj )← minFPi

∈ fj .return (xj , yj)

Distributed Bayesian Model Algorithm (DBMA) I

Centralized ApproachSEE

P( l |FPi ) =P (FPi | l)P(l)

P(FPi), P(FPi ) �= 0 (5)

P ( lt | lt−1) =

∫P(lt | l ′t−1

′t−1) dl

′t−1 (6)

where lt is the current location and lt−1 is the previous location.

Distributed Bayesian Model Algorithm (DBMA) II

Distributed Approach

Initialize gi = 0.while pi is assigned and received, do

for all FP′j ∈ fj , do

gi(j)← P( j | {FP1,FP2, . . . ,FPn})end for

end whilereturn gi

Distributed Neural Networks (DNN) I

Types of Neural Networks

Multilayer Perceptron

Generalized Regression

Both will require a supervised learning to train the network.

Figure: Artificial Neural Network (ANN) Architecture

Distributed Neural Networks (DNN) II

Figure: WDCN with neural networks

WDC-based Fingerprinting System Overview

System Overview

Experimental Setup

Hardware and Software

The Radio Map

WDC-based Fingerprinting System Experimental Setup

Experimental Setup

Figure: System block diagram

WDC-based Fingerprinting System Hardware and Software

Hardware

Figure: USRP2 with custom WBX daughterboard

WDC-based Fingerprinting System Hardware and Software

Software

WDCN communications - GNU Radio

Fingerprint position processing - Python

Web-based user interface - PHP

ORIGIN

WDC-based Fingerprinting System The Radio Map

The Radio Map

Radio Map for 1st Floor ICTAS

-25 N15

0 5 10 15 20 25 30 35 40 45

Position Number

Figure: Radio Map with 45 Positions

Algorithm Performance and Results

Algorithm Performance and Results Algorithm Evaluation

Algorithm Evaluation I

Comparison of Distributed Localization Algorithms

0 20 40 60 80 100 120 140 160 180 200 2203

X−direction (ft.)

Distributed Neural Network − MLP

Actual PathEstimated Path

0 20 40 60 80 100 120 140 160 180 200 2203

X−direction (ft.)

Distributed Neural Network − GR

0 20 40 60 80 100 120 140 160 180 200 2203

X−direction (ft.)

Distributed Markov

0 20 40 60 80 100 120 140 160 180 200 2203

X−direction (ft.)

t.)Distributed Euclidean

Figure: Comparison of solutions of distributed localization algorithms

Algorithm Performance and Results Algorithm Evaluation

Algorithm Evaluation II

50 100 150 20010

Error Radius (ft)

DEDADBMAGRNNMLPNN

Figure: Performance comparision of distributed localization algorithms

Algorithm Performance and Results Error Statistics

Error Statistics

Error statistics of distributed localization algorithmsAlgorithm Minimum Error Mean Error Max Error

DEDA 0 ft. 10.81 ft. 55 ft.

DBMA 0 ft. 35.33 ft. 220 ft.

GRNN 0 ft. 14.95 ft. 95 ft.

MLP 0 ft. 16.90 ft. 155 ft.

Average 0 ft. 19.50 ft. 131.25 ft.

Position Location Demo Overview

Overview

Functional Workflow of WDC process

Video of Demo

Position Location Demo Functional Workflow

Task dissemination and retrieval I

Figure: Phase I: Task dissemination

Position Location Demo Functional Workflow

Task dissemination and retrieval II

Figure: Phase II: Task retrieval

Position Location Demo Demo

Fingerprinting Position System

Position Location Demo Computational Complexity

Computational Complexity of Online Phase

Single node

Algorithm Computation Searching Sorting

EDA O(n) N/A O(n log n)BMA O(n) O (n (log u + 1)) O(n log n)

WDC slave node

Algorithm Computation Searching Sorting

DEDA O(n/4) N/A O(n/4 log n/4)DBMA O(n/4) O (n/4 (log u + 1)) O(n/4 log n/4)

Concluding Remarks Conclusions

Conclusions

Successful location estimates are highly dependent on quality anduniqueness of RF fingerprints.

Increasing spatial granularity of fingerprint positions does notnecessarily improve performance of position estimation.

Distributed PL is beneficial for large service areas with largedatabases.

De-centralized computations removes single-point of failure andsecurity intrusions.

Concluding Remarks Future Work

Future Work

Examine optimization techinque of multisplitting for conventional PLtechniques.

Expand distributed sensor system to all CORNET nodes and createmobile WDCN.

Implement demo with new UHD driver for USRP2.

Implement neural network for WDCN.

Concluding Remarks Future Work

Questions

Distributed Localization for Wireless Distributed Networks in Indoor Environments

Technology

Transcript of Distributed Localization for Wireless Distributed Networks in Indoor Environments

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