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On Crowd-sensing back-end

This paper is devoted to the crowd sensing applications. Crowd sensing (mobile crowd sensing in our case) is a new sensing paradigm based on the power of the crowd with the sensing capabilities of mobile devices, such as smartphones or wearable devices. This power is based on the smartphones, usually equipped with multiple sensors. So, it enables to collect local information from the individual’s surrounding environment with the help of sensing features of the mobile devices. In this paper, we provide the review of the back-end systems (data stores, etc.) for mobile crowd sensing systems. The main goal of this review is to propose the software architecture for mobile crowd sensing in Smart City environment. We discuss also the deployment of cloud-back-ends in Russia.

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On Crowd-sensing back-end

  1. 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. 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. 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. 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. 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. 6. Local DB with replications
  7. 7. Local DB with replications • SQLite as local DB • Cloud based data store: Dropbox
  8. 8. Lambda architecture • An immutable sequence of records is captured and fed into a batch system and a stream processing system in parallel.
  9. 9. Data Streaming support • Apache Flink • Flume • Chukwa • Kafka
  10. 10. Client Side applications • Quarks • ETSI ISG: mobile edge computing • Cisco: fog computing
  11. 11. Crowd sensing for video data
  12. 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. 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. 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)

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