Title:

Development of a Proactive Context-aware Prompt Mechanism for Location-based Crowdsourcing in Sensafety

Description:

Location-based crowdsourcing has evolved as a way of continuous data collection from volunteers in a cost-effective and fast way. This applies especially when it comes to measuring the urban quality of life, which underlies fast-paced changes. Since it is mainly based on voluntary participation, methodologies to motivate the users to participate are needed. This thesis aims to provide an approach to proactively prompt users to participate based on their current context.
After analyzing the related work in the fields of crowdsourcing, location-based services, and user prompts, the Sensafety application is introduced. It is an example of a location-based crowdsourcing application. Since data collection is an essential topic for applications of this kind, it is argued that Sensafety is a suitable platform to develop such a prompting mechanism upon.
After designing a concept and developing an exemplary implementation of context-aware participation prompts, it is evaluated via a user study. In all steps, the focus lies on maximizing the data collection while minimizing the prompts’ disruptiveness. The study shows that analyzing the user context to find suitable moments for prompts is a viable approach. On the one side, the collected data helped to increase the geographical coverage of the Sensafety data. On the other, users stated low levels of annoyance by the notifications and no feeling of being disturbed.

Supervisor: Sandro Rodriguez Garzon [1]

Type:  Master Thesis

Duration: 6 months