Aggregating Sensing Data for Mobile Crowdsourcing

PhD Thesis Proposal Defence


Title: "Aggregating Sensing Data for Mobile Crowdsourcing"

by

Mr. Xinglin ZHANG


Abstract:

Mobile crowdsourcing applications are becoming more and more prevalent in 
recent years, as smartphones equipped with various built-in sensors are 
proliferating rapidly. The large quantity of potential sensing data stimulates 
researchers to probe into large-scale tasks that used to be costly or 
impossible, such as noise pollution monitoring and traffic surveillance. Yet 
the efficient aggregation of the crowdsourced data, which is of essential 
importance for such sensing tasks, has not received sufficient attention. 
Specifically, two main challenges of data aggregation are investigated in this 
proposal: how to motivate normal users to contribute sensing data and how to 
conduct robust inference from sensing data.

The low participation level of smartphone users due to various resource 
consumptions, such as time and power, remains a hurdle that prevents the 
enjoyment brought by crowd sensing applications. Recently, some researchers 
have done pioneer works in motivating users to contribute their resources by 
designing incentive mechanisms, which are able to provide certain rewards for 
participation. However, none of these works considered smartphone users' nature 
of opportunistically occurring in the area of interest. Specifically, for a 
general smartphone sensing application, the platform would distribute tasks to 
each user on her arrival and has to make an immediate decision according to the 
user's reply. To accommodate this general setting, we propose to design online 
incentive mechanisms based on online reverse auction.

On the other hand, the low-quality crowdsourced data are prone to containing 
outliers that may severely impair the mobile crowdsourcing applications. Thus 
in this proposal, we conduct pioneer investigation considering crowdsourced 
data quality. Specifically, we focus on estimating user motion trajectory 
information, which plays an essential role in multiple crowdsourcing 
applications, such as indoor localization, context recognition, indoor 
navigation, etc. We resort to the family of robust statistics and design a 
robust trajectory estimation scheme, which is capable of alleviating the 
negative influence of abnormal crowdsourced user trajectories, differentiating 
normal users from abnormal users, and overcoming the challenge brought by 
spatial imbalance of crowdsourced trajectories.

Theoretic properties of the designed methods are analyzed. Also, thorough 
simulations and experiments are conducted to further verify the efficiency of 
these methods in aggregating sensing data for mobile crowdsourcing.


Date:			Wednesday, 26 March 2014

Time:                   10:00am - 12:00noon

Venue:                  Room 4480
                         lifts 25/26

Committee Members:	Dr. Lei Chen (Supervisor)
 			Dr. Yunhao Liu (Supervisor)
 			Dr. Raymond Wong (Chairperson)
 			Dr. Pan Hui
 			Dr. Ke Yi


**** ALL are Welcome ****