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

Meihua Wang, Wei-Chang Yeh, Ta-Chung Chu, Xianyong Zhang, Chia-Ling Huang and Jun Yang
Additional contact information
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

Energies, 2018, vol. 11, issue 9, 1-23

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.

Keywords: smart sensor network; wireless smart sensor network; fuzzy energy consumption; activity on arc; swarm intelligence algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/9/2385/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/9/2385/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:9:p:2385-:d:168927

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().