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
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•
•
•
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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
Road map of Chicago
Paths of Chigago Twitteres
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Mobile|Phone Sensing
• Revolution driven by smart devices to collect
of crowd activity traces
Richdistribution
suitespopularity
of sensors
Increasing
App
channels
Microphone
Compas
Accelerometer
Camera
WIFI/3G/4G
GPS
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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
Crowd
Sensing
Microphone Camera
WIFI/3G/4G
Compas
Accelerometer GPS
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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 scales
Individual
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
8
Problematic
• Real world deployment is labor-intensive
process
Crosscutting
challenges
Privacy
Energy
Task
description
Worker
recruitment
Task
deployment
Worker
rewarding
Data upload
Task
execution
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Existing tools
• Funf in a box
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Existing tools
• Pogo : Middleware for Mobile Phone Sensing
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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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Limitation of the SotA
• Community sensing problem
Data redundancy
High energy consumption
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Challenges summary
Development cost
Software
challenges
Crosscutting
challenges
Scalability
Flexibility
Privacy
Security
Energy
Task
description
Worker
recruitment
Task
deployment
Worker
rewarding
Data upload
Task
execution
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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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APISENSE® – The platform
http://www.apisense.fr
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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,
Warning log Taxonomy
application:
apps.process(log.pid).applicationName,
topTask: apps.topTask().applicationName
Error 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 phase
dialog.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 phase
time.schedule({ period: '5s' }, function() {
trace.add({
position: model.evaluate(attributes(buffer)),
stats: model.statistics() });
Representative Confusion Matrix
buffer = new Array();
} } });
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Agenda
•
•
•
•
•
CrowdSensing
Problematic & Limitation
The APISENSE platform
Preliminary results
Demo
27
Questions ?
Nicolas H ADERER
C HRISTOPHE R IBEIRO
Romain R OUVOY
http://apisense.fr
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