Paper Info
Title
Distributed Schemes for Crowdsourcing-Based Sensing Task Assignment in Cognitive Radio Networks.
Abstract
Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals' positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm's convergence.
Year
DOI
Venue
2017
10.1155/2017/5017653
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Field
DocType
Volume
Convergence (routing),Wireless,Crowdsensing,Crowdsourcing,Computer science,Markov chain,Computer network,Distributed algorithm,Optimization problem,Cognitive radio,Distributed computing
Journal
2017
ISSN
Citations 
PageRank 
1530-8669
3
0.40
References 
Authors
8
3
Name
Order
Citations
PageRank
Linbo Zhai152.13
Hua Wang27614.82
Chengcheng Liu344.49