It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is relevant to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network connectivity or GPS signal) results in a large number of small segments, where many of these segments actually belong together. This work proposes a novel segmentation approach that opportunistically fills gaps in a user's location trace by borrowing location data from other co-located users utilizing the power of mobile crowd sensing and computing (MCSC) paradigm. Through our analysis of four separate large-scale crowd sensing study datasets, we show that our approach yields more and larger segments than the state-of-the-art, where each segment accurately represents the presence of a user at a significant personal place.
To View the Abstract Contents
Or Enquire Now !!!, WISEN Project Specialist will contact you soon.
Now it is Your Time to Shine.
Great careers Start Here.
We Guide you to Every Step
Success! You're Awesome
Thank you for filling out your information!
We’ve sent you an email with your Final Year Project PPT file download link at the email address you provided. Please enjoy, and let us know if there’s anything else we can help you with.
To know more details Call 900 31 31 555
The WISEN Team