Large Scale Data Collection, Storage and Analysis Using Connected and Autonomous Vehicles

Junaid Ahmed Khan

Abstract: The growth in the number of mobile devices today result in an increasing demand for large amount of rich multimedia content to support numerous applications. It is however challenging for the current cellular networks to deal with such increasing demand, both in terms of cost and bandwidth that are necessary to handle the “massive” content generated and consumed by mobile users in an urban environment. The technological advancement in modern vehicles allow us to harness their computing, caching and communication capabilities to supplement the infrastructure network. Intelligent vehicles today can collaboratively collect, store, process and share heterogeneous data on urban streets and facilitate citizens with different services. We will introduce two novels schemes, VISIT and SAVING for the socially-aware data collection and storage on vehicles respectively with the aim of bringing content closer to the urban mobile users and reduce bandwidth demand and cost.

Bio: Junaid Ahmed Khan is a postdoctoral research associate at the Center for Urban Science and Progress (CUSP) and C2MART,  New York University. Prior to this, he was a research fellow at the DRONES and Smart City research clusters at the FedEx Institute of Technology, University of Memphis from January 2018 to August 2019. He was a postdoctoral researcher at Inria Agora team, CITI lab at the National Institute of Applied Sciences, Lyon, France from October 2016 to January 2018. He has a Ph.D in Computer Science from Université Paris-Est, Marne-la-Vallée, France in November 2016. His research interests are Connected Autonomous Vehicles (CAVs), Fog networking, Internet of Things (IoTs), Information-Centric Networks (ICN/CCN) and Complex/Social networks analysis.

Fast Trajectory Search for Real-World Applications in Smart Cities

Sheng Wang

Abstract: With the popularity of smartphones equipped with GPS, a vast amount of trajectory data are being produced from location-based services, such as Uber, Google Maps, and Foursquare. Such kind of trajectory data can be broadly divided into three types: 1) commuter trajectories from taxicabs and ride-sharing apps; 2) vehicle trajectories from GPS navigation apps; 3) activity trajectories from social network check-ins and travel blogs.
In this talk, Sheng will briefly introduce efficient and effective search on each of the three types of trajectory data, each of which has a real-world application. In particular: 1) commuter trajectory search can serve for the transport capacity estimation and route planning; 2) vehicle trajectory search can help real-time traffic monitoring and trend analysis; 3) activity trajectory search can be used in interactive and personalized trip planning. More technical details can be found in Sheng’s Ph.D. thesis. At the end of the talk, Sheng will share his new thoughts on building a general data-analytic framework for real-time smart cities.

Bio: Dr. Sheng Wang is currently a smart cities postdoc at the CUSP of New York University, supervised by Prof. Juliana Freire and Prof. Daniel B. Neill. He obtained his Ph.D. from RMIT University, Australia, on August 2019, supervised by Dr. Zhifeng BaoProf. J. Shane Culpepper, and Prof. Timos Sellis. He was a visiting scholar of Prof. Gao Cong at Nanyang Technological University, Singapore. Before that, he got his master and bachelor degrees from Nanjing University of Aeronautics and Astronautics, China in 2016 and 2013, respectively. His interests are mainly on database, especially trajectory data management. Since 2017, he has published several papers on top venues in database and information systems as the first author, such as SIGMOD, PVLDB, SIGIR, WSDM, ICDE, TKDE. He won two best paper awards, two ACM travel awards, and a 3 Minute Thesis Competition. More information about Sheng can be found from his homepage: