随着配备高保真传感器的移动设备的普及以及无线网络资费的快速下降，空间众包作为一种问题解决框架被用于解决将位置相关的任务（如路况报告、食品配送）分配给工人（配备智能设备并愿意完成任务的人）的问题.研究空间众包中最优任务分配问题，关键在于设计出将每个任务分配给最合适的工人的任务分配策略，以使得完成的总任务数目最大化，而所有的工人可以在完成所分配的任务后，在预期最晚工作时间之前返回起点.找到全局最优分配是一个棘手的问题，因为该问题不等于单个工人的最佳分配的简单累加.注意到，仅有部分工人存在任务依赖，因此利用树分解技术将工人分割成独立的集合，并提出一种带启发式的深度优先搜索算法，该算法可以快速地更新启发函数界限，从而高效地对不可能成为最优解的分配方案尽早地进行剪枝.实验结果表明：所提出的方法是非常有效的，可以很好地解决最优任务分配问题.

The ubiquity of mobile devices with high-fidelity sensors and the sharp decreases in the cost of ultra-broadband wireless network flourish the market of spatial crowdsourcing, which has been proposed as a new framework to assign location-aware tasks (e.g., reporting road traffic, delivering food) to workers (i.e., persons equipped with smart device and willing to perform tasks). This paper studies the task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized while all workers can go back to their starting point before expected deadlines after performing assigned tasks. It is an intractable problem since optimal assignment for individual worker does not necessarily lead to global optimal results. Observing that the task assignment dependency only exists amongst subsets of workers, this study utilizes tree-decomposition technique to separate workers into independent clusters and develops an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. Extended experiments demonstrate the effectiveness and efficiency of the proposed solution.

TP311