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CPAC: Energy-Efficient Algorithm for IoT Sensor Networks Based on Enhanced Hybrid Intelligent Swarm

Qi Wang1,*, Wei Liu1, Hualong Yu1, Shang Zheng1, Shang Gao1, Fabrizio Granelli2
1 Jiangsu University of Science and Technology, Zhenjiang, 212003, China.
2 University of Trento, Via Sommarive 9, Povo TN 38123, Italy.
* Corresponding Author: Qi Wang. Email: wangqi@just.edu.cn.

Computer Modeling in Engineering & Sciences 2019, 121(1), 83-103. https://doi.org/10.32604/cmes.2019.06897

Abstract

The wireless sensor network (WSN) is widely employed in the application scenarios of the Internet of Things (IoT) in recent years. Extending the lifetime of the entire system had become a significant challenge due to the energy-constrained fundamental limits of sensor nodes on the perceptual layer of IoT. The clustering routing structures are currently the most popular solution, which can effectively reduce the energy consumption of the entire network and improve its reliability. This paper introduces an enhanced hybrid intelligential algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) method. The enhanced PSO is deployed to select the optimal cluster heads for establishing the clustering architecture. An improved ACO is introduced to realize the data transmission from terminal sensor nodes to the base station. Our proposed algorithm can effectively reduce the entire energy consumption and extend the lifetime of IoT sensor networks. Compared with the traditional algorithms, the simulation results show that the presented novel algorithm in this paper has obvious optimization and improvement in network lifetime and energy utilization efficiency.

Keywords

Internet of Things, wireless sensor network, particle swarm optimization, ant colony optimization, energy efficiency

Cite This Article

Wang, Q., Liu, W., Yu, H., Zheng, S., Gao, S. et al. (2019). CPAC: Energy-Efficient Algorithm for IoT Sensor Networks Based on Enhanced Hybrid Intelligent Swarm. CMES-Computer Modeling in Engineering & Sciences, 121(1), 83–103.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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