Server Design to Ensure Quality and Fairness in Mobile Crowdsourcing

Abstract: Mobile crowdsourcing solves complex problems by utilizing the untapped power of a crowd, connected through the fantastic mobile devices we use in our daily life. These gadgets are equipped with a versatile set of sensor that could be used for gathering data about a specific location in combination with questions to the human carrier. Common problems in crowdsourcing systems is how to ensure that the contributed data from the crowd is of a high quality and how to do task allocation fairly. A large population of users is often needed to ensure a high quality of data and coverage, every participant is important and the system have to do be designed with the population in mind.In this thesis we discuss the responsibilities of the server in a crowdsourcing system and presents a system model which tries to tackle the problems of quality and fairness. A prototype of the system model was developed (CrowdS) to determine its potential, for both Android and iOS devices.A long running test was performed to evaluate the performance of CrowdS with the main focus on determine how well the system performed in terms of coverage of the search area and fairness of earnings and prices. The test was executed on both platforms for a couple of weeks. The vast majority of all completed tasks were finished within 10 minutes of being created, with a median time of 3 minutes and 32 seconds seconds. Jain’s fairness index measured an overall high fairness for both the price of tasks at 0.944 and the earnings made by participants at 0.941. The radius of the search area had to be extended to maximum of 800 meters for roughly on third of the tasks to find the required number of participants.