More with Less:Lowering User Burden in Mobile Crowdsourcing through Compressive Sensing

Presenter: Harshitha

Chidanan

da

Liwen Xu , Xiaohong Hao , Nicholas D. Lane , Xin

Liu , Thomas Moscibroda – UbiComp 2015

CS290F: Smartphone centric systems and applications

o Introductiono Main problemo Definitiono Main pointso Contributionso Other Solutions

o Challengeso Key Technical pointso Evaluationo Strengths and

weaknesseso Open Issueso Thoughts and summary

Table Of Contents

Analyzing is criticalAnalysis has opened up new domains

Main problem

Making analysis requires large amount of data High burden placed on the user

Users won’t join the system Users stop participating eventually

Definition

Mobile crowdsourcing is a promising way to collect largescale data about ourselves and the urban areas we live in

Sensor data from environment(noise) User provided information

Main points and contributions

Present compressive crowdsensing (CCS) – a framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios.

Reduced amounts of manually collected data Acceptable levels of overall accuracy

First time CS has been applied to mobile crowdsourcing

CS has the ability to utilize inherent structure that may not be obvious /natural ways of considering the data

Other solutions

Already known approaches : General-purpose statistical methods

Sub-sampling Interpolation

Domain-specific techniques Population surveying Geospatial

All of these methods presuppose, and then leverage, certain relationships within the collected data.

CS has the ability to utilize inherent structure that may not be obvious and may not correspond to more “natural” ways of considering the data

Conventional CS Deal with 1-D vectors Easily vectorized data

Crowdsourcing datasets

Multiple columns Multi-dimensional

Unknown as to how to apply CS to crowdsourced dataNovel processing steps need to be introduced: Complex data correlation preserve the important ones base training handle missing data

Challenges

Key Technical Points

Demonstrate the feasibility of applying CS to large-scale question-based user surveys

Propose a technique to use the data that do not have obvious representations

Evaluate compressive crowdsourcing by applying real-world datasets

COMPRESSIVE SENSING PRIMERCompressive Sensing (CS), an efficient technique of sampling data with an underlying sparse structure

Example

Traffic speeds Speeds at intersections Reduces sampling rate

-Sparse Structure-Random Sampling-Data Reconstruction-Base Learning-Stages of CS

Sparse structure

Signal of interest x ->coefficient vector

Names kx is called k-sparse k non-zero entriesy is called compressible small

< - - 5-sparse

Sampling is the reduction of a continuous signal to a discrete signal

Most popular CS sampling methods

Random Sampling

Random Sampling

To Capture signal y• m samples• n entries• m<<n

25 random readings ~ 100 readings

Data reconstruction

Linear interpolation

Linear interpolation

Compressive sensing

Base Learning

Base Ψ plays a critical role in transforming the signal of interest y to a sparse signal x

Sparsifying Base can be:

Standard : fourier base Discrete cosine transform Good base: trained using historical data

Stages of CS

Count rat sightings in different areas of a city within a particular period of time. • Use a set of

historical data {y1, y2, · · · , yN}

• Select a small random sample of areas

• Recover the data

Stages

COMPRESSIVE CROWDSENSINGA framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios.

Crowdsourced data

A global view of a phenomenon

Data through sensors

Manually entered data

Step 1: Data structure conversion

Step 2: Base training

Uncovers the inherent correlation in the data structure

K-SVD algorithm is used

Given data Y, finds base:1)Represents each yi=Ψxi

2)Minimizes total error

Step 3: Sampling

Passively Users provide data

when they wish Contributed data is

grouped Sampling group Training group

Only data from users who have information is used during reconstruction

Pro-actively Selects randomly within

the range of sampling values

Users with characteristics are directly asked to provide data

If such characteristics about the user are not known then all users are asked.

Step 4: Reconstruction

Arranging a matrix representation according to:• Training• Sampling

Matrix is projected into the trained base to recover a sparse representation of the target

Missing target values is recovered by multiplying base with the recovered sparse representation

Evaluation

Methodology: diverse group of real life datasets perform the same random sampling

Evaluation Metric: Data in vector format Format

Comparison Baselines Conventional CS Linear Interpolation Spline Interpolation Kriging Interpolation Sampling Only

Conventional CS

Conventional CS-exact same CS stages as CCS

Linear Interpolation

• A method of curve fitting using linear polynomials.

Spline interpolation

Curve fitting is performed but this time using piecewise cubic splines.

Kriging Interpolation

A well-known method for geographical interpolation that is popular in the GIS community

Sensing data can be represented more accurately with more coefficients and their corresponding bases

Strengths and Weaknesses

Strengths

First to use compressive sensing for crowdsourcing

Succeeds Good results Diversity of datasets

Temporal Spatial Demographic

Weaknesses

If original data shows no correlation, CS would not apply.

Overall vector space may be very large.

Within broader CS research, how to predict dataset performance is unknown

Open issues/ Directions for future scope

Analysis across different datasets with different crowd-based scenarios

Analysis out of limited correlated data. Target a variety of application domains and monitor:

Traffic conditions Place categories Noise pollution Wifi conditions Happiness

Thoughts and summary

Thoughts Very very hard to

understand Required lot of

background reading Needed better term

explanation Less graphical

representation Good results

Summary Recovers large-scale

urban information Two fold

Demonstrates novel crowd-based applications of compressive sensing

Develops key new techniques that allow CS to be generically applied to many scenarios

Thank You!QUESTIONS?