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Dynamic Deployment of Sensing Experiments in the Wild U sing Smartphones
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Transcript of Dynamic Deployment of Sensing Experiments in the Wild U sing Smartphones
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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
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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