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Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

Author

Listed:
  • Meihua Wang

    (College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510633, China)

  • Wei-Chang Yeh

    (Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan)

  • Ta-Chung Chu

    (Department of Industrial Management and Information, Southern Taiwan University of Science and Technology, Tainan 700, Taiwan)

  • Xianyong Zhang

    (Department of Automation, Guangdong Polytechnic Normal University, Guangzhou 510633, China)

  • Chia-Ling Huang

    (Department of Logistics and Shipping Management, Kainan University, Taoyuan 33857, Taiwan)

  • Jun Yang

    (School of Mathematics, South China University of Technology, Guangzhou 510633, China)

Abstract

Wireless (smart) sensor networks (WSNs), networks made up of embedded wireless smart sensors, are an important paradigm with a wide range of applications, including the internet of things (IoT), smart grids, smart production systems, smart buildings and many others. WSNs achieve better execution efficiency if their energy consumption can be better controlled, because their component sensors are either difficult or impossible to recharge, and have a finite battery life. In addition, transmission cost must be minimized, and signal transmission quantity must be maximized to improve WSN performance. Thus, a multi-objective involving energy consumption, cost and signal transmission quantity in WSNs needs to be studied. Energy consumption, cost and signal transmission quantity usually have uncertain characteristics, and can often be represented by fuzzy numbers. Therefore, this work suggests a fuzzy simplified swarm optimization algorithm (fSSO) to resolve the multi-objective optimization problem consisting of energy consumption, cost and signal transmission quantity of the transmission process in WSNs under uncertainty. Finally, an experiment of ten benchmarks from smaller to larger scale WSNs is conducted to demonstrate the effectiveness and efficiency of the proposed fSSO algorithm.

Suggested Citation

  • Meihua Wang & Wei-Chang Yeh & Ta-Chung Chu & Xianyong Zhang & Chia-Ling Huang & Jun Yang, 2018. "Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm," Energies, MDPI, vol. 11(9), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2385-:d:168927
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    References listed on IDEAS

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    Cited by:

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