Multi-Objective Optimization Based Allocation of Heterogeneous Spatial Crowdsourcing Tasks - 2018

PROJECT TITLE :

Multi-Objective Optimization Based Allocation of Heterogeneous Spatial Crowdsourcing Tasks - 2018

ABSTRACT:

With the speedy development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, that refers to recruiting mobile employees to perform location-based mostly tasks, has gained emerging interest from both research communities and industries. In this Project, we tend to contemplate a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task includes a valid length, operation complexity, budget limitation, and the quantity of required employees. Every volunteer worker completes assigned tasks whereas conducting his/her routine tasks. The system includes a desired task chance coverage and budget constraint. Underneath this situation, we investigate an vital downside, specifically heterogeneous spatial crowdsourcing task allocation (HSC-TA), which strives to look a collection of representative Pareto-optimal allocation solutions for the multi-objective optimization downside, such that the assigned task coverage is maximized and incentive value is minimized simultaneously. To accommodate the multi-constraints in heterogeneous spatial crowdsourcing, we tend to build a worker mobility behavior prediction model to align with allocation process. We tend to prove that the HSC-TA drawback is NP-laborious. We tend to propose effective heuristic ways, as well as multi-round linear weight optimization and enhanced multi-objective particle swarm optimization algorithms to attain adequate Pareto-optimal allocation. Comprehensive experiments on each real-world and artificial data sets clearly validate the effectiveness and efficiency of our proposed approaches.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here