Location Familiarity Oriented Task Planning in Spatial Crowdsourcing Spatial crowdsourcing (SC) is a promising framework for requesting workers (e.g., smartphone users) to perform locationbased tasks (e.g., taking scenic photos or checking product placement). Specifically, to perform the assigned task, the worker needs to move physically to the target location, hence his familiarity with the target location has an impact on the efficiency and quality of the task completion. On the other hand, the SC server usually intends to finish all tasks with a low cost. Considering these factors, in this paper, we study a BiObjective Task Planning (BOTP) problem in SC, where the workers are assigned and scheduled to perform the appropriate tasks, such that the workersu0027 familiarity with the locations of the spatial tasks and the SC serveru0027s cost for recruiting workers are jointly optimized. We prove that the BOTP problem is NPhard and thus intractable. To tackle the BOTP problem, we propose a heuristic algorithm based on the multiobjective simulated annealing algorithm. The extensive experiments demonstrate the effectiveness of our proposed algorithm over a realworld dataset.

Unknown Worker Recruitment with Budget and Covering Constraints for Mobile Crowdsensing Mobile crowdsensing, through which a requester can recruit a group of crowd workers via a platform and coordinate them to perform some sensing tasks, has attracted lots of attention recently. However, most of the existing mobile crowdsensing systems assume that the qualities of workers are known in advance. Based on this assumption, they study the task assignment and worker recruitment problems. Unfortunately, the qualities of workers are generally unknown in reality, so the platform must find the tradeoff between exploring and exploiting the qualities by using reinforcement learning. At the same time, all sensing tasks are required to be covered in each round (covering constraint), and the requester usually has a limited budget (budget constraint). In this paper, we study how to recruit unknown workers under the budget and covering constraints so that the total expected achieved qualities can be maximized. To this end, we model the problem as a combination of a maximum weight matching problem and a special multiarmed bandit problem. We first consider that the recruitment costs of workers are homogeneous and propose a recruitment algorithm with a performance guarantee. Then, we study the heterogenous case and devise a heuristic algorithm. Finally, we demonstrate the performances of our algorithms through extensive simulations.