基于数据冗余控制的移动群智感知任务分配方法

移动群智感知系统中任务之间存在时空覆盖重叠性，这可能导致重复数据收集从而引发数据冗余问题，为此，提出了一种可同时控制任务内以及任务间数据冗余的任务分配方法。该方法首先提出基于长短期记忆（LSTM）神经网络的轨迹序列预测模型，对任务参与者进行细分时空单元的轨迹序列预测，然后根据轨迹预测结果提出最小化数据冗余的优化模型。通过最小化时空单元的数据冗余度来控制单个任务内的数据冗余问题，并通过让单个任务参与者在时空单元中的感知数据被最大化重复利用来控制多个任务之间时空覆盖重叠性带来的数据冗余。实验结果表明，提出的任务分配方法可以有效地减少任务内及任务间的数据冗余。

Task allocation method based on data redundant control in mobile crowd sensing

Due to the overlap of time and space coverage between tasks in mobile crowd sensing, repeated data collection may happen and cause data redundancy problem. In view of this, this paper proposed a task allocation method to reduce data redundancy within and between tasks. Firstly, this method designed a trajectory sequence prediction model based on the long short-term memory(LSTM) neural network, to predict trajectory sequences of task participants within subdivided spatial-temporal units. Then based on the trajectory prediction results, the method proposed an optimization model to minimize data redundancy. Specifically, the optimization model constrained the data redundancy within a single task by minimizing the data redundancy metric in each spatial-temporal unit, and limited the data redundancy between multiple tasks by maximizing the reuse of the sensing data of each task participant in a spatial-temporal unit. Experimental results verify that the proposed task allocation method can effectively reduce the data redundancy within and between tasks.