GeoCrowd: a spatial crowdsourcing system implementation

The increasing usage of smartphones, along with the ubiquitous internet access, brought to the research and market community a new type of crowdsourcing: spatial crowdsourcing. With spatial crowdsourcing each task is related to a specific geographical location (e.g., latitude, longitude) and in order to be executed, the assigned workers need to physically travel at the task's location. ❧ In this thesis project, a novel spatial crowdsourcing system is introduced, coined GeoCrowd. GeoCrowd focuses on a specific mode of spatial crowdsourcing, named Server Assigned Tasks (SAT) mode. In this mode the server's goal is to solve the Maximum Task Assignment (MTA) problem (i.e., maximize the assignments of available workers to tasks), while satisfying their constraints. Next, the three core components of the system and their implementation details are presented: 1) a web application, 2) a mobile application on Android and 3) a background process that runs the assignment algorithm. The current algorithmic solution is based on a flow network representation which can be efficiently solved (i.e. O(VE²)) by computing its maximum flow when running the Edmonds‐Karp algorithm. ❧ Subsequently, we present the integration of GeoCrowd in a multimedia platform, named MediaQ, which is used to collect crowdsourced mobile videos from the public. ❧ As the popularity of MediaQ platform expands to different universities, a parallel, distributed version of maximum flow is investigated to provide a scalable solution based on the MapReduce programming model. ❧ Finally, we introduce new constraints to the MTA problem which makes the problem NP-Hard. A novel hybrid algorithm is proposed that uses different heuristics to solve the MTA problem.