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Mobile Crowdsensing Application

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 s alvatore.distefano@polimi.it. FIA - Athens - March 18, 2014. Agenda.

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Mobile Crowdsensing Application

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  1. 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 salvatore.distefano@polimi.it FIA - Athens - March 18, 2014

  2. Agenda • Introduction • Crowd-based approaches • CrowdSensing • Mobile CrowdSensing • MCSaaS • MCS Application

  3. Introduction • 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

  4. 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

  5. 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 …

  6. 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

  7. 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 !!!

  8. MCSStack

  9. 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. • ……

  10. 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

  11. MCS Challenges Localized Analytics Resource Limitations Privacy Aggregate Analytics Architecture

  12. MCS as a service - MCSAAS

  13. MCSaaS: a Cloud platform for deploying MCS apps on SAaaS infrastructure • readilyavailable infrastructure: • a platform provider only needs booking resources for MCS, sending client-side 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

  14. 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-sideload from infrastructure-level stats collection • optionalon-demand feature, may be disabled at will • lower strain on constrained resources

  15. Mobile Crowdsensing application: PotHole Detector

  16. Mobile Crowdsensing application: PotHole Detector

  17. Q&A • THANKS!

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