Detail

Mobile crowd-sensing and Big Data analytics for smart urban mobility

In this talk I will present the application of mobile crowd-sensing para­digm and Big Data analytics in supporting efficient, safe, green and healthy mobility in urban environments. Smart mobile devices with integrated sensing capabilities, and people using them representing “human sensors”, can be efficiently used to crowd source and sense diverse information in domains that are relevant to urban life and mobility, such as traffic status, air quality, citizens’ health and activities, etc.

We have developed the CitySensing platform consisting of mobile application components, server/cloud components and services and visualization/analytics (dashboard) components organized in a distributed architecture to support devel­opment of applications for various Smart City scenarios.

The processing and analytics of these massive amount of crowd sourced/sensed data, Big mobility Data at the edge and within the cloud infrastructure, provides detection of situations and events that influence people mobility in urban environment, and dissemination of notifications and recommended actions. We have evaluated contemporary methods, algorithms and software systems for management, processing, analysis, and mining of Big crowd-sensed and mobility data on a cluster and cloud infrastructure, and base our Big Data solutions on open source software, such as Apache MapReduce/Hadoop, Apache Spark, Storm, Kafka, WEKA, etc.