THESIS
2017
xiii, 150 pages : illustrations ; 30 cm
Abstract
Recently, Location-based services (LBSs) refer to the services that are based on the location (spatial)
data, which bring conveniences to our daily life and challenges to both industry and academia.
LBSs include the “spatial crowdsourcing” service, which allows requesters to post spatial tasks
to specified locations then the crowd workers will move to the locations of the assigned tasks to
conduct, the “location-based mobile advertising” service, which helps the vendors to push advertisements
to potential customers near to the shops, and the “search nearby” service, which queries
some points of interests (POI, e.g., hotels, parks, museums) near a location. Among the LBSs,
online-to-offline (O2O) is a widely applied mechanism, where users join activities, plan travel
routes and o...[
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Recently, Location-based services (LBSs) refer to the services that are based on the location (spatial)
data, which bring conveniences to our daily life and challenges to both industry and academia.
LBSs include the “spatial crowdsourcing” service, which allows requesters to post spatial tasks
to specified locations then the crowd workers will move to the locations of the assigned tasks to
conduct, the “location-based mobile advertising” service, which helps the vendors to push advertisements
to potential customers near to the shops, and the “search nearby” service, which queries
some points of interests (POI, e.g., hotels, parks, museums) near a location. Among the LBSs,
online-to-offline (O2O) is a widely applied mechanism, where users join activities, plan travel
routes and order goods online, then perform the according actions offline. To support this fundamental
mechanism, task assignment and scheduling are necessary and important, which match or
schedule the tasks to users under the constraints (e.g., spatial-temporal constraints, capacity constraints,
budget constraints).
In this thesis, we study task assignment and scheduling techniques in three practical problems in
the spatial crowdsourcing area, namely the Utility-Aware Ridesharing on Road Networks problem,
which matches riders to vehicles and schedules the routes for vehicles with a goal of maximizing
the overall utility of riders (i.e., vehicle-related utility, rider-related utility and trajectory-related utility) subject to the constraints of the deadlines of riders and the capacities of the vehicles, the
Reliable Diversity-Based Spatial Crowdsourcing problem, which assigns spatial workers to spatial
tasks to maximize the completion reliability and the spatial/temporal diversities of spatial tasks
subject to the constraints of the valid periods of tasks and the working areas of the workers, and the
Multi-Skill Spatial Crowdsourcing problem, which assigns spatial workers to multi-skill required
tasks to finish as many tasks as possible and to minimize the total travel cost of workers.
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