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Energies 2018, 11(9), 2385; https://doi.org/10.3390/en11092385

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

1
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510633, China
2
Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan
3
Department of Industrial Management and Information, Southern Taiwan University of Science and Technology, Tainan 700, Taiwan
4
Department of Automation, Guangdong Polytechnic Normal University, Guangzhou 510633, China
5
Department of Logistics and Shipping Management, Kainan University, Taoyuan 33857, Taiwan
6
School of Mathematics, South China University of Technology, Guangzhou 510633, China
*
Author to whom correspondence should be addressed.
Received: 20 July 2018 / Revised: 25 August 2018 / Accepted: 27 August 2018 / Published: 10 September 2018
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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. View Full-Text
Keywords: smart sensor network; wireless smart sensor network; fuzzy energy consumption; activity on arc; swarm intelligence algorithm smart sensor network; wireless smart sensor network; fuzzy energy consumption; activity on arc; swarm intelligence algorithm
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Wang, M.; Yeh, W.-C.; Chu, T.-C.; Zhang, X.; Huang, C.-L.; Yang, J. Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm. Energies 2018, 11, 2385.

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