Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data

• Main Authors: Bowen Du, Qian Tao, Feng Zhu, Tianshu Song
• Format: Article
• Language: English
• Published: Hindawi Limited 2017-01-01
• Series: Journal of Sensors
• Online Access: http://dx.doi.org/10.1155/2017/8568613

These days, with the increasingly widespread employment of sensors, particularly those attached to vehicles, the collection of spatial data is becoming easier and more accurate. As a result, many relevant areas, such as spatial crowdsourcing, are gaining ever more attention. A typical spatial crowdsourcing scenario involves an employer publishing a task and some workers helping to accomplish it. However, most of previous studies have only considered the spatial information of workers and tasks, while ignoring individual variations among workers. In this paper, we consider the Software Development Team Formation (SDTF) problem, which aims to assemble a team of workers whose abilities satisfy the requirements of the task. After showing that the problem is NP-hard, we propose three greedy algorithms and a multiple-phase algorithm to approximately solve the problem. Extensive experiments are conducted on synthetic and real datasets, and the results verify the effectiveness and efficiency of our algorithms.