Transcript 3.MCS-App-FIA-Workshop-Distefano

Mobile Crowdsensing, Social and Big Data as Innovation Enablers for
Future Internet Cloud-based Architectures and Services
Mobile Crowdsensing
Application
Salvatore Distefano
Politecnico di Milano – Italy
[email protected]
FIA - Athens - March 18, 2014
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Agenda
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Introduction
Crowd-based approaches
Crowd Sensing
Mobile Crowd Sensing
MCSaaS
MCS Application
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Introduction
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20-30 billions of devices by 2020
IoT: enhanced communication techniques
New challenges
High level solutions for managing things
New-value added applications directly involving
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Crowd-based approaches
• Leveraging on crowd
• Data, services, ideas, contents, skills, money, … coming
from crowds
• Crowdsourcing = Crowd + outsourcing
• “the practice of obtaining something by contributions
from a large group of people and especially from the
online community rather than from traditional employees
or suppliers”
• Crowdfunding, crowdsearching, crowdsensing, open
source development
• Volunteer contribution: free vs by charge
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Crowdsensing
• Crowdsourcing on data
• Two possible ways
• Direct, participatory contribution on a volunteer basis
• Data are provided by sensors/sensing resources from contributors
• Active, a priori, both proactive and reactive, runtime
• Traffic monitoring, pothole mapping, emergency/disaster prediction,
management and recovery, VGI, …
• Indirect
• DB, Web, Social Networks, Crowdsourcing/searching, data mining,
feature extraction, filtering, processing, …
• Passive, a posteriori, reactive, offline
• Investigation of the effect/impact of a given phenomenon on a given area,
geocomputing …
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User at Front End
Mobile Crowdsensing
• The integration of sensors
that can be used for
gathering materialistic or
non-materialistic information
• Involve people that both
participate and use the MCS
• Geo-tagged info
Web Service at Back End
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The MCS Paradigm
Participatory
Sensing
Opportunistic
Sensing
Users actively engage in the
data collection activity.
Takes random sample which is
application defined.
Users manually determine how,
when, what, where to sample.
Easy to gather large amount
data in small time.
Can avoid phone context issues.
Can’t avoid phone context
issues.
Higher burdens or costs.
Lower burdens or costs if
contextual problems are
handled.
Filtering Data by Handling Privacy Issues & Localization.
Dataset is ready for research !!!
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App
End Users
MCSStack
MCS Application
Server
MCS App/
Service
Provider
Noden
Node1 Node2
Node3
Contributors
MCS App
Client
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Mobile Crowdsensing Applications
Monitoring common phenomenon…
•Pollution (air/noise) levels in a neighborhood.
•Real-time traffic patterns.
•Pot holes on roads.
•Road closures and transit timings.
•……
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Mobile Crowdsensing: current issues
volunteer enrolment:
• requires out-of-band campaign (social network) to get attention
• involves user-initiated activity (website download) to begin contributing
• slow and unpredictable uptake
app/service availability/reliability:
• degradation with node churn
• real-time info may translate into severe burden on resources (battery)
• privacy
• customisability
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MCS Challenges
Localized Analytics
Resource Limitations
Privacy
Aggregate Analytics
Architecture
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MCS as a service - MCSAAS
App1
End Users
MCS
App 2
MCS
App 1
MCS App/
Service 1
Provider
Appm
End Users
App2
End Users
MCS
App m
MCS App/
Service 2
Provider
MCSaaS
FrontEnd
Server
MCS App/
Service m
Provider
MCSaaS
SAaaS
Server FE
SAaaS
IaaS
DaaS
Infrastructure
Infrastructure Provider
Provider
Contributor 1
MCSaaS
Provider
Infrastructure
Provider
Noden
Node1
Contributor n
Node2
Node3
Contributor 2
Contributor 3
MCSaaS
Client
MCS App
Client
SAaaS
Client
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MCSaaS: a Cloud platform for deploying
MCS apps on SAaaS infrastructure
readily available infrastructure:
• a platform provider only needs booking resources for MCS, sending clientside platform code
• SAaaS will take care of (one-time) client deployment
automatic deployment:
• fire-and-forget experience for the app provider - just send a request to
MCSaaS provider for resources, attaching the payload
• (SAaaS-unaware) dissemination carried out by the platform
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MCSaaS: a Cloud platform for managing
MCS apps on SAaaS infrastructure
churn management(s), each at its own layer:
• transparent
• built-in, as part of the framework(s) management
real-time info:
• built-in, platform-level sharing of monitoring data
• low device-side load from infrastructure-level stats collection
• optional on-demand feature, may be disabled at will
• lower strain on constrained resources
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Mobile Crowdsensing application: PotHole
Detector
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Mobile Crowdsensing application: PotHole
Detector
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Q&A
THANKS!