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Wireless Communications and Mobile Computing
Volume 2018, Article ID 9821946, 11 pages
https://doi.org/10.1155/2018/9821946
Research Article

Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks

1College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
2Key Laboratory of Vibration and Control of Aero-Propulsion System of Ministry of Education, Northeastern University, Shenyang 110819, China
3College of Jangho Architecture, Northeastern University, Shenyang 110819, China
4State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China

Correspondence should be addressed to Yuhuai Peng; nc.ude.uen.liam@iauhuygnep and Qingxu Deng; nc.ude.uen.liam@xqgned

Received 8 September 2017; Accepted 3 January 2018; Published 21 February 2018

Academic Editor: Kuan Zhang

Copyright © 2018 Dawei Shen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Currently, a number of crowdsourcing-based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real-time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software-defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA) is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.