LOCATION PREDICTION ENHANCED TASK ASSIGNMENT IN SPATIAL CROWDSOURCING

MPhil Thesis Defence


Title: "LOCATION PREDICTION ENHANCED TASK ASSIGNMENT IN SPATIAL CROWDSOURCING"

By

Mr. Ziyuan ZHAO


Abstract

The ubiquity of mobile devices has brought the popularity of a new problem 
solving mechanism - spatial crowdsourcing, which utilizes the power of crowds 
to accomplish location-specific tasks.

Many spatial-crowdsourcing-based applications have emerged and deeply 
influenced the our daily life, such as taking taxi, package dispatching and 
food delivering. Many unified and standardized crowdsourcing services adopt the 
server assigned tasks(SAT) mode, in which the system proactively assigns tasks 
to workers in proximity of requested locations. Under this task assignment 
mode, the travel cost between workers and tasks becomes of vital importance, 
because less travel cost means less response time and higher task acceptance 
ratio.

In this thesis, we formally define the minimum travel cost assignment problem 
in spatial crowdsourcing. Since we consider the total travel cost during a 
period of time, we explore the possible locations of future tasks to assist 
task assignment planning. By adopting various task-distribution prediction 
algorithms, we propose several effective and scalable approaches. We conducted 
a comprehensive experimental evaluation on both synthetic data and real-world 
data to compare the performance and effectiveness of our proposed solutions.


Date:			Monday, 24 August 2015

Time:			9:30am - 11:30am

Venue:			Room 2132B
 			Lift 19

Committee Members:	Dr. Lei Chen (Supervisor)
 			Dr. Ke Yi (Chairperson)
 			Dr. Pan Hui


**** ALL are Welcome ****