Target allocation of WSN based on Parallel Chaotic Elite Quantum-Inspired Evolutionary Algorithm

Jie Zhou, Eryk Dutkiewicz, Ren Ping Liu, Gengfa Fang, Yuanan Liu

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    1 Citation (Scopus)

    Abstract

    The target allocation problem is one of the important challenges in WSNs as sensor nodes have limited sensing and communication capabilities. In the target allocation problem, a set of targets is selected for each sensor to improve the monitoring quality as well as the energy efficiency. However, the target allocation problem is a combinatorial optimization problem, and the computational complexity is too high to consider all combinations for practical implementation. In this paper, we propose a novel Parallel Chaotic Elite Quantum-Inspired Evolutionary Algorithm (PCEQEA) for target allocation problem in WSNs. The PCEQEA combines the advantages of elite genetic algorithm and quantum-inspired evolutionary algorithm. It achieves high parallel search performance and fast convergence to global optimum solution. Simulation results demonstrate that proposed PCEQEA improves WSN detection coverage by detecting more targets than other existing schemes.

    Original languageEnglish
    Title of host publication15th International Symposium on Communications and Information Technologies (ISCIT 2015)
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages287-290
    Number of pages4
    ISBN (Electronic)9781467368209
    DOIs
    Publication statusPublished - 2015
    Event15th International Symposium on Communications and Information Technologies, ISCIT 2015 - Nara, Japan
    Duration: 7 Oct 20159 Oct 2015

    Other

    Other15th International Symposium on Communications and Information Technologies, ISCIT 2015
    Country/TerritoryJapan
    CityNara
    Period7/10/159/10/15

    Keywords

    • Resource management
    • Wireless sensor networks
    • Monitoring
    • Genetic algorithms
    • Chaotic communication
    • Sensors
    • Evolutionary computation

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