School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Key Laboratory of Optical Communication and Networks, Chongqing 400065, China.
Sensors (Basel). 2019 Oct 27;19(21):4666. doi: 10.3390/s19214666.
Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate.
移动众包感知（MCS）系统通常吸引大量参与者，他们的感知成本和兴趣偏好差异很大，需要执行任务，其中准确的任务分配起着不可或缺的作用，也面临着许多挑战（例如，如何简化复杂的任务分配过程，提高任务和参与者之间的匹配准确性，同时保证提交数据的可信度）。为了克服这些挑战，我们在本文中提出了一种基于服务收益感知的多任务分配（SBAMA）策略。首先，基于参与者的任务难度、任务历史、感知能力和感知积极性，对参与者的服务收益进行建模，以满足各种任务类型的差异化需求。随后，通过增强的模糊聚类方法对用户进行聚类。最后，设计了一个梯度下降算法来将任务类型分配给参与者，以实现最大的服务收益。仿真结果验证了所提出的任务分配策略不仅有效地降低了匹配复杂度，而且提高了任务完成率。
