Aggregating Sensing Data for Mobile Crowdsourcing

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis 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, we investigate the following crucial challenges of data 
aggregation in this thesis:  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 thesis, 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.

Most of mobile sensing applications rely on inference components heavily for 
detecting interesting activities or contexts.  Existing work implements 
inference components using traditional models designed for balanced data sets, 
where the sizes of interesting (positive) and non-interesting (negative) data 
are comparable. Practically, however, the positive and negative sensing data 
are highly imbalanced. Therefore, we propose a new inference framework SLIM 
based on several machine learning techniques in order to accommodate the 
imbalanced nature of sensing data.

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 
applications.


Date:			Thursday, 10 July 2014

Time:			1:00pm - 3:00pm

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Chun Man CHAN (CIVL)

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. Cunsheng Ding
 			Prof. Pan Hui
 			Prof. Jialin Yu (FINA)
                        Prof. Jiannong Cao (Comp., PolyU)


**** ALL are Welcome ****