Project information

• Category: Framework Design/ System
• Project date: 2016-2018

Location traces on a space-time continumm of any moving agents namely, people, vehicles are termed as trajectory. ``Human moves with an intent" and capturing the inherent knowledge behind any movement may help to facilitate several location based services or personalized recommendation systems. Therefore, one of the major objective of the work is to perceive the correlations among location, time and mobility behaviours of people. However, analysing huge volume of mobility data is time and compute-intensive. To handle huge amount of data, cloud, fog, edge are the most significant infrastructures to provide delay and energy efficient solution. This work aims to address following research questions:

• How to analyse huge amount of mobility traces and extract usable knowledge to provision better urban-transportation planning and smart living? Whether cloud-fog-edge based paradigm provides effective solution?
• How to model peoples' movement behaviour to facilitate several context-aware services effectively? Whether the behavioral differences in the movement patterns of the individuals can be captured and utilized to cluster/ categorize users?
• How to extract mobility-association rules (inter-sequence and inter-transactional) from trajectory log to summarize interesting movement behavior?
• Can we map the mobility knowledge of one known region to another unknown (target) region of similar type (say, academic campus) for traffic, point-of-interest (POI)/land-use predictions etc.?

The mobility analysis reveals the interconnections, correlations and differences among individuals' movement behaviors and their activities by exploring their mobility attributes. However, analyzing huge volume of accumulated trajectory traces is still a challenging task. The major objective of the work is to develop a cloud-based framework to extract informative knowledge from these mobile-trajectories and utilize the information in several applications.