Paper Info
Title
Participant selection for t-sweep k-coverage crowd sensing tasks
Abstract
AbstractWith the popularization of wireless networks and mobile intelligent terminals, mobile crowd sensing is becoming a promising sensing paradigm. Tasks are assigned to users with mobile devices, which then collect and submit ambient information to the server. The composition of participants greatly determines the quality and cost of the collected information. This paper aims to select fewest participants to achieve the quality required by a sensing task. The requirement namely "t-sweep k-coverage" means for a target location, every t time interval should at least k participants sense. The participant selection problem for "t-sweep k-coverage" crowd sensing tasks is NP-hard. Through delicate matrix stacking, linear programming can be adopted to solve the problem when it is in small size. We further propose a participant selection method based on greedy strategy. The two methods are evaluated through simulated experiments using users' call detail records. The results show that for small problems, both the two methods can find a participant set meeting the requirement. The number of participants picked by the greedy based method is roughly twice of the linear programming based method. However, when problems become larger, the linear programming based method performs unstably, while the greedy based method can still output a reasonable solution.
Year
DOI
Venue
2018
10.1007/s11280-017-0481-x
Periodicals
Keywords
Field
DocType
Crowdsensing,t-sweep k-coverage,Participant selection,Linearprogramming,Set covering
Wireless network,Data mining,Computer science,Matrix (mathematics),Mobile device,Linear programming,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
21
3
1386-145X
Citations 
PageRank 
References 
2
0.38
28
Authors
5
Name
Order
Citations
PageRank
Zhiwen Yu12753220.67
Jie Zhou220.38
Wenzhong Guo361176.01
Longkun Guo465.49
Zhiwen Yu5462.97