Location-aware Task Assignment and Routing in Mobile Crowd Sensing

2020 ◽  
Author(s):  
Shathee Akter ◽  
Seokhoon Yoon
Keyword(s):  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Location Aware ◽  
Mobile Crowd

Mobile crowd sensing : task assignment, privacy protection, incentive, and application

2021 ◽  
Author(s):  
Zhigang Jia ◽  
Weiwei Zhao ◽  
Jie Luo ◽  
Yang Chen ◽  
ChangJing Xu
Keyword(s):  
Privacy Protection ◽  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

2018 ◽  
Vol 66 (11) ◽  
pp. 5526-5538 ◽  
Author(s):  
Zhenyu Zhou ◽  
Junhao Feng ◽  
Bo Gu ◽  
Bo Ai ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Route Planning ◽  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

QoS-constrained sensing task assignment for mobile crowd sensing

2014 ◽  
Author(s):  
Zhijie Wang ◽  
Dijiang Huang ◽  
Huijun Wu ◽  
Yuli Deng ◽  
Ailixier Aikebaier ◽  
...  
Keyword(s):  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

scholarly journals Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4666 ◽  
Author(s):  
Zhidu Li ◽  
Hailiang Liu ◽  
Ruyan Wang
Keyword(s):  
Task Assignment ◽  
Task Completion ◽  
Mobile Crowd Sensing ◽  
Match Task ◽  
Crowd Sensing ◽  
Fuzzy Clustering Method ◽  
Gradient Descent Algorithm ◽  
Assignment Strategy ◽  
Task Types ◽  
Mobile Crowd

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.

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