An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets

In citizen science, participants’ productivity is imperative to project success. We investigate the feasibility of a collaborative approach to citizen science, within which productivity is enhanced by capitalizing on the diversity of individual attributes among participants. Specifically, we explore the possibility of enhancing productivity by integrating multiple individual attributes to inform the choice of which task should be assigned to which individual. To that end, we collect data in an online citizen science project composed of two task types: (i) filtering images of interest from an image repository in a limited time, and (ii) allocating tags on the object in the filtered images over unlimited time. The first task is assigned to those who have more experience in playing action video games, and the second task to those who have higher intrinsic motivation to participate. While each attribute has weak predictive power on the task performance, we demonstrate a greater increase in productivity when assigning participants to the task based on a combination of these attributes. We acknowledge that such an increase is modest compared to the case where participants are randomly assigned to the tasks, which could offset the effort of implementing our attribute-based task assignment scheme. This study constitutes a first step toward understanding and capitalizing on individual differences in attributes toward enhancing productivity in collaborative citizen science.

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An Adaptive Task Assignment Scheme for Data Service in Heterogeneous Vehicular Networks

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) ◽

10.1109/nas.2018.8515735 ◽

2018 ◽

Author(s):

Penglin Dai ◽

Kai Liu ◽

Ke Xiao ◽

Junhua Wang ◽

Zhaofei Yu ◽

...

Keyword(s):

Vehicular Networks ◽

Task Assignment ◽

Data Service ◽

Assignment Scheme

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Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning

Symmetry ◽

10.3390/sym13122417 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2417

Author(s):

Pengxing Zhu ◽

Xi Fang

Keyword(s):

Heuristic Algorithms ◽

Learning Algorithm ◽

Task Assignment ◽

Complex Environments ◽

Task Execution ◽

Q Learning ◽

Assignment Model ◽

Distribution Process ◽

Aerial Vehicle ◽

Assignment Scheme

Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.

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