Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-06-06 Cooperative journals: 《计算机应用研究》
Abstract： To achieve high efficiency with limitation of energy consumption is a key problem in the system of UAVs. In existing UAV task allocation methods, the relevance between tasks and UAV cruise directions is neglected, which may further influence the energy consumption and delay of task accomplishment. In view of this, this paper proposed an UAV task allocation method based on the relevance analysis between tasks and cruise directions. This method includes two phases: task screening phase and conflict resolution phase based on consensus. In the first phase, this method selects out tasks without turning back for an UAV according to angles between the cruise direction of the UAV and directions of these tasks, then designs an algorithm to further select candidate tasks before interaction from the tasks without turning back according to their energy consumption utility parameters and time urgency parameters. In the second phase, this method solves tasks conflicts between UAVs after exchanging of their candidate tasks according to energy consumption utility parameters and time delay parameters of these tasks in different cruise directions of these UAVs. Simulation results verify that the proposed method can achieve lower average task energy consumption and average task delay.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-10 Cooperative journals: 《计算机应用研究》
Abstract： The drawbacks of existing task recommendation in mobile crowd sensing were as follows: on the one hand, not fully considering the influence of spatial-temporal information on worker preference led to low accuracy of recommendation; On the other hand, ignoring the impact of task popularity on recommendation led to poor recommendation coverage. To solve these drawbacks, this paper proposed a novel task recommendation approach based on spatial-temporal information and task popularity analysis in mobile crowd sensing. Firstly, this approach made full use of the relevant information contained in the worker execution record (e. g. , the time and location of worker performing tasks) to accurately predict the preference of worker for performing tasks. Secondly, in order to reduce the impact of popular tasks on recommendation coverage, this paper analyzed task popularity based on worker reputation and task execution record, and designed appropriate task popularity penalty factor. Then, combining worker preference and task popularity penalty factor, this paper provided an appropriate task recommendation list for each worker. Finally, the experimental results show that compared with the existing baseline methods, the proposed method improves the recommendation accuracy by 3.5% and the recommendation coverage by 25%.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-04-07 Cooperative journals: 《计算机应用研究》
Abstract： 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.
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