This document discusses crowd sensing backends and architectures for mobile crowd sensing applications. It describes common architectures that include local databases on devices with cloud replication, lambda architectures combining batch and stream processing, and support for data streaming technologies. It also covers crowd sensing for video data and various mobile backends and backend-as-a-service providers. Finally, it discusses practical use cases for crowd sensing deployments in Russia.

1. On Crowd Sensing Back-
end
Dmitry Namiot, Manfred Sneps-Sneppe
Lomonosov Moscow State University, AbavaNet
dnamiot@gmail.com,
manfreds.sneps@gmail.com
DAMDID 2016

2. Data persistence for crowd sensing
applications
• Crowd sensing as a new sensing
paradigm
• The power of the crowd with the sensing
capabilities of mobile devices.
• Review of the back-end systems (data
stores, etc.) for mobile crowd sensing
systems.
• The software architecture for mobile crowd
sensing in Smart City environment.

3. Content
• Crowd sensing tasks
• The common architecture for mobile
crowd sensing
• Crowd sensing for video data
• Mobile back-ends
• On practical use-cases and deployment in
Russia

4. Introduction
• The main challenges:
user participation,
anonymity, privacy
and security, data
sensing quality,
trustworthiness of the
contributed data
• Our target: data
stores for crowd
sensing.
Mobile Crowd Sensing -
the power of the crowd
mobile users (mobile
devices) with the sensing
capabilities

5. The common architecture
• Minimal intrusion on client devices. The
mobile device computing overhead always
must be minimized.
• The fast feedback and minimal delay in
producing stream information.
• Openness and security.
• Complete data management workflow.

6. Local DB with replications

7. Local DB with replications
• SQLite as local DB
• Cloud based data
store: Dropbox

8. Lambda architecture
• An immutable
sequence of records
is captured and fed
into a batch system
and a stream
processing system in
parallel.

9. Data Streaming support
• Apache Flink
• Flume
• Chukwa
• Kafka

10. Client Side applications
• Quarks
• ETSI ISG: mobile
edge computing
• Cisco: fog computing

11. Crowd sensing for video data

12. Crowd sensing for video
• Apache Cluodstack
• Eucalyptus (Elastic
Utility Computing
Architecture for
Linking Your
Programs To Useful
Systems)
• OpenStack
• OpenStack Object
Storage (Swift)

13. Mobile back-ends
• The key moment - the
simplicity for mobile
developers
• Data Storage is a part of
MBaaS
• Convertigo
• FIWARE
• Kurento
• Mobile Backend As A
Service (MBaas) -
backend cloud storage
for developers
• MBaaS provides APIs
SDKs for mobile
developers.
• MBaaS provides such
features as user
management, push
notifications, integration
with social networking
services.

14. On practical use-cases and
deployment in
Russia
• Our prototype for radio map:
Kafka > Spark Streaming > Cassandra
• The personal data should be stored on the
territory of the Russian Federation
• There are no Amazon S3
• For many sensors data storage is a part of
the system (e.g. Bluetooth tags)