Improve Energy Consumption and Signal Transmission Quality of Routings in Wireless Sensor Networks

Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.198254-198264
Main Authors: Yeh, Wei-Chang, Jiang, Yunzhi, Huang, Chia-Ling, Xiong, Neal N., Hu, Cheng-Feng, Yeh, Yuan-Hui
Format: Article
Language:English
Subjects:
Algorithms
Energy consumption
Erbium
Internet of Things
Mathematics
network reliability
Optimization
Particle swarm optimization
Performance enhancement
Reliability
routing
Sensors
Signal quality
Signal transmission
Smart buildings
Smart grid
Smart sensors
Wireless communication
Wireless networks
Wireless sensor network
Wireless sensor networks
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Summary:Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments in the modern age. Recent advances in WSNs have resulted in the rapid development of sensors. However, WSNs will only able to achieve better execution efficiencies if their energy consumption - owing to limited battery life and difficulty of recharging - can be better controlled. Moreover, signal transmission quality determines WSN performance. Hence, two main concerns - energy consumption and signal transmission quality - should be addressed to improve the performance of WSNs. Thus, a new bi-objective simplified swarm optimization algorithm (bSSO) is proposed by employing the concepts of simple routing, SSO, and crowd distance. The performance and applicability of the proposed bSSO using eight different parameter settings are demonstrated through an experiment involving ten WSN benchmarks ranging from 100 to 1000 sensors. The proposed algorithm is then compared with NSGA-II, which is an algorithm widely used to solve multi-objective problems. The results show that the proposed bSSO can successfully achieve the aim of this work.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3030629