Dynamic Deployment of Sensing Experiments in the Wild Using Smartphones

Nicolas Haderer, Christophe Ribeiro, Romain Rouvoy, Lionel Seintuier

University Lille 1 – LIFL,Inria Lille – Nord Europe

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Agenda

• CrowdSensing• Problematic & Limitation• The APISENSE platform• Preliminary results• Demo

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Why do we collect data?

• Better understanding of crowd behavior and its environment– E.g., optimizing public transport services

Paths of Chigago TwitteresRoad map of Chicago

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Mobile|Phone Sensing

• Revolution driven by smart devices to collect of crowd activity traces

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Increasing popularityApp distribution channels

GPSWIFI/3G/4G

AccelerometerCompas

CameraMicrophone

Rich suites of sensors

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CrowdSensing

• Capability of lifting a (large) diffuse group of participants to delegate the task of retrieving trustable data from the field

GPSWIFI/3G/4G

AccelerometerCompas

CameraMicrophone

Crowd Sensing

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CrowdSensing Data collection can take different forms

Participatory sensing involves the user in the sensing task (eg. surveys)

Opportunistic sensing uses mobile sensors carried by the user (eg. smartphones)

And can be effective across multiple scalesIndividual Group Community

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CrowdSensing : opportunities

• A lot of research interests – Building Noise Map Urban area– Obtain human crowd density – Earthquakes Monitoring – …

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Agenda

• CrowdSensing• Problematic & Limitation• The APISENSE platform• Preliminary results• Demo

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Problematic

• Real world deployment is labor-intensive process

Taskdescription

Taskexecution

Workerrecruitment

Taskdeployment

Data uploadWorker rewarding

Crosscutting challenges

Privacy Energy

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Existing tools

• Funf in a box

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• Pogo : Middleware for Mobile Phone Sensing

Existing tools

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Limitation of the SotA

• Multi-tenant architectures are limited– Constrained infrastructures (database, resources)– Tenants side effects (availability)– Legal issues (data ownership)– Security leaks (data isolation)

• Application-specific solutions– Lack of flexibility to be reused in another context

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• Community sensing problem

Limitation of the SotA

High energy consumption

Data redundancy

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Challenges summary

Taskdescription

Taskexecution

Workerrecruitment

Taskdeployment

Data uploadWorker rewarding

Crosscutting challenges

Privacy Energy

Softwarechallenges Scalability SecurityFlexibility

Development cost

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Agenda

• CrowdSensing• Problematic & Limitation• The APISENSE platform• Preliminary results• Demo

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APISENSE®

• Open crowd-sensing platform– Flexible and customizable architecture– Supporting various research communities– Leveraging the deployment of CrowdSensing tasks

http://www.apisense.fr 17

APISENSE® – The platform

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Central Node

• A trustable central server– Intermediary between collector node & workers– Guarantees workers anonymity (generated ids)– Checks the task scripts and rewards workers– Deploy and generate sensing node

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Sensing Node

• A cloud-oriented storage• Support for data authentication + encryption• Configurable infrastructure (SPL + components)

• XML | NoSQL database, processing services, visualization

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APISENSE Mobile Application

• Mobile application– Downloads & executes scripts (sandbox)– Uploads datasets when plugged– Controls sensor privileges & privacy filters

Time filter Sensors privileges Location filter

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CrowdSensing Javascript API

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CrowdSensing Javascript API

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APISENSE®

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Agenda

• CrowdSensing• Problematic & Limitation• The APISENSE platform• Preliminary results• Demo

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Application Example

• Collecting exceptions in the wild (CoffeeScript)

logcat.onLog {filter: [’*:W’,’*:E’]}, (log) -> trace.add message: log.message, time: log.timestamp, application: apps.process(log.pid).applicationName, topTask: apps.topTask().applicationName

Error log Taxonomy

Warning log Taxonomy

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Application Example

• Assessing Machine Learning Models– User context recognition implementation : ~ 30 lines

…accelerometer.onChange(function(acc) { buffer.push(acc) });// Learning phasedialog.display({ message: "Select movement", spinner: classes },function(pattern){ accelerometer.onChange(function(acc) { buffer.push(acc) }); sleep(‘5s’) model.record(attributes(buffer), pattern); buffer = new Array(); return;});…// Exploitation phasetime.schedule({ period: '5s' }, function() { trace.add({ position: model.evaluate(attributes(buffer)), stats: model.statistics() }); buffer = new Array();} } });

Representative Confusion Matrix

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Agenda

• CrowdSensing• Problematic & Limitation• The APISENSE platform• Preliminary results• Demo

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Nicolas HADERERCHRISTOPHE RIBEIRORomain ROUVOYLionel SEINTURIER

Questions ?

http://apisense.fr