9th International Conference on Communications and Networking in China

Research Article

An Approximate Dynamic Programming Model for Link Scheduling in WMNs with Gateway Design Constraint

  • @INPROCEEDINGS{10.4108/icst.chinacom.2014.256365,
        author={Chien-Liang Chen and Wan-Yu Liu and Shu-Huai Chang and Chun-Cheng Lin},
        title={An Approximate Dynamic Programming Model for Link Scheduling in WMNs with Gateway Design Constraint},
        proceedings={9th International Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2015},
        month={1},
        keywords={wireless mesh network link scheduling gateway genetic algorithm dynamic programming approximate dynamic programming},
        doi={10.4108/icst.chinacom.2014.256365}
    }
    
  • Chien-Liang Chen
    Wan-Yu Liu
    Shu-Huai Chang
    Chun-Cheng Lin
    Year: 2015
    An Approximate Dynamic Programming Model for Link Scheduling in WMNs with Gateway Design Constraint
    CHINACOM
    IEEE
    DOI: 10.4108/icst.chinacom.2014.256365
Chien-Liang Chen1, Wan-Yu Liu1, Shu-Huai Chang1, Chun-Cheng Lin2,*
  • 1: Aletheia University
  • 2: National Chiao Tung University
*Contact email: cclin321@nctu.edu.tw

Abstract

The design objective of wireless mesh networks is generally focused on transmitting data packets as many as possible in a given time. Wireless mesh network models may be categorized into many different types according to different programming objectives, wherein the link scheduling model achieves the purpose of effective transmission of packets by certain transmission constraints and the programming of the open status of packet transmission links in each stage. In this paper, the approximate dynamic programming approach for a new wireless mesh network scheduling model having node programming information saving locations and network link gateway designs is orchestrated. The experiment results show that, in addition to maintaining many wireless network characteristics, the scheduling algorithm is effectively executed and approximate dynamic programming effectively simulates dynamic programming and has performances superior to genetic algorithm.