Distributed Localization for Wireless Distributed Networks in Indoor Environments

Distributed Localization for Wireless Distributed
Networks in Indoor Environments

Hermie P. Mendoza

Wireless @ VT
Virginia 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 a
global 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 power Extends total network
consumption per node lifetime
2 Efficient load balancing Better resource demand
across collaborating nodes and supply matching
3 Harnesses available Meets computational
network resources latency requirements of
4 Robust, secure, & fault complex processing tasks
tolerant execution Attain stringent QoS
5 Simplifies radio’s form requirements
factor Economic cost savings


Preliminaries Location Awareness for WDC Paradigms

Location Awareness for WDC Paradigm

Improve overall wireless
communication system
Needed to achieve
interoperability

Figure: Cognitive radio sensing
environment


Preliminaries PL and WDC

Motivations I
Localization is generally accomplished in a centralized manner at the
expense of a single network node’s resources. Can the problem of
positioning 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
Goal
Minimize the time taken to compute the individual localization
calculations.

Problem Formulation
Given a set of J of m jobs and a set of N of n nodes, the
processing time for a job j ∈ J on node i ∈ N is pij ∈ Z+ . Then
we must ﬁnd an assignment of the jobs J to the nodes N such
that the makespan, or the completion time, is minimized.


Preliminaries Min Makespan

Min Makespan Problem II

Integer programming formulation

minimize t
subject to xij = 1, j ∈J
i ∈N
(1)
xij pij ≤ t, i ∈N
j∈J

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 and
estimate its position in a distributed manner.

Goal
Distributed algorithms must be ﬂexible and applicable for various
ﬁngerprint-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


Deterministic positioning method
Euclidean
n
distance 2
L2 = FPi − FPi (4)
Bayesian i =1
modeling
n
Neural 2
(ˆ, y ) = min
x ˆ FPi − FPi (5)
Networks FPi
i =1




Pattern Recognition

Euclidean
distance
Bayesian
modeling
Neural
Networks



Distributed Target Localization I
Distributed Localization Approaches
Transfering computationally complex operations to a single node with
greater 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
(ˆ, y )
x ˆ estimated position of user
gi vector of probabilities calculated by node i
FPi tuple of RSS at ﬁngerprint location i
ˆ
FP i vector of distances calculated by node i
FPi RSS received at sensor i
FPi RSS database entry of sensor i
pi AOR or partition assigned to a node i



Distributed Euclidean Distance Algorithm (DEDA) I
Centralized Approach

f
2
(ˆ, y ) = min
x ˆ FPi − FPi (4)
FPi
i =1



Distributed Euclidean Distance Algorithm (DEDA) II

Distributed Approach
ˆ
Initialize FPi = 0.
while pi is assigned and received, do
for all FPj ∈ fj , do
ˆ j 2
FP i ← k=1 FPk − FPk
end for
end while
(xj , yj ) ← minFPi ∈ fj .
ˆ ˆ
return (xj , yj )
ˆ ˆ



Distributed Bayesian Model Algorithm (DBMA) I
Centralized Approach
SEE
P ( FPi | l ) P(l )
P( l | FPi ) = , P(FPi ) = 0 (5)
P(FPi )
ACT
P ( lt | lt−1 ) = P lt | lt−1 P(lt−1 ) dlt−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 FPj ∈ fj , do
gi (j) ← P( j| {FP1 , FP2 , . . . , FPn })
end for
end while
return 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


WDC-based Fingerprinting System The Radio Map

The Radio Map

Radio Map for 1st Floor ICTAS
0

-5

-10
10

N22
N21
-15
N20
N19
RSS (dB)

N18
-20
N17
N16
-25 N15
N14
N13
-30 N12
N11

-35

-40
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 Performance and Results Algorithm Evaluation

Algorithm Evaluation I

Comparison of Distributed Localization Algorithms
Distributed Euclidean Distributed Markov
Y−direction (ft.) 5 5

Y−direction (ft.)
4 4

Actual Path Actual Path
Estimated Path Estimated Path
3 3
0 20 40 60 80 100 120 140 160 180 200 220 0 20 40 60 80 100 120 140 160 180 200 220
X−direction (ft.) X−direction (ft.)

Distributed Neural Network − GR Distributed Neural Network − MLP
5 5
Y−direction (ft.)

Y−direction (ft.)
4 4

Actual Path Actual Path
Estimated Path Estimated Path
3 3
0 20 40 60 80 100 120 140 160 180 200 220 0 20 40 60 80 100 120 140 160 180 200 220
X−direction (ft.) X−direction (ft.)

Figure: Comparison of solutions of distributed localization algorithms


Algorithm Performance and Results Algorithm Evaluation

Algorithm Evaluation II

100

90

80

70
Percentage (%)

60

50

40

30
DEDA
DBMA
20 GRNN
MLPNN
10
50 100 150 200
Error Radius (ft)

Figure: Performance comparision of distributed localization algorithms


Algorithm Performance and Results Error Statistics

Error Statistics

Error statistics of distributed localization algorithms
Algorithm 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 and
uniqueness of RF fingerprints.
Increasing spatial granularity of fingerprint positions does not
necessarily improve performance of position estimation.
Distributed PL is beneficial for large service areas with large
databases.
De-centralized computations removes single-point of failure and
security intrusions.


Concluding Remarks Future Work

Future Work

Examine optimization techinque of multisplitting for conventional PL
techniques.
Expand distributed sensor system to all CORNET nodes and create
mobile 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

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