Optimal cluster head localization for cluster-based wireless sensor network using free-space optical technology and genetic algorithm optimization

• Published in: Journal of ambient intelligence and humanized computing Vol. 15; no. 10; pp. 3693 - 3713
• Main Author: Hamouda, Yousef E. M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Bit error rate; Clusters; Communications systems; Computational Intelligence; Energy consumption; Engineering; Fitness; Free space optics; Free-space optical communication; Fuzzy logic; Genetic algorithms; Installation costs; Internet access; Links; Localization; Network topologies; Optimization; Original Research; Parameters; Rain; Robotics and Automation; Sensors; Signal to noise ratio; Snow; Spectrum allocation; Topology; User Interfaces and Human Computer Interaction; Weather; Weighting; Wireless communications; Wireless sensor networks; Wireless sensor network; Network topology; Clustering; Genetic algorithm; Free space optical
• DOI: 10.1007/s12652-024-04849-0
• ISSN: 1868-5137

Free Space Optical (FSO) is a wireless communication technology that is distinguished from other communication systems by several advantages including license free of operating spectrum, high data rate, low installation cost, and fast deployment. FSO is employed in many applications including Internet and mobile services links. Nevertheless, FSO link quality is affected by weather conditions including fog, rain, and snow. The main challenge of the FSO channel is the dynamic fluctuating of these weather conditions which degrade the link quality and reduces the data rate. Therefore, the development of robust FSO link topology is a crucial issue to overcome the bad and severe weather conditions. Cluster-based Wireless Sensor Network (WSN) arranges the network into groups called clusters where one Cluster Head (CH) is selected to manage the communication activities inside the group. CHs localization is the main challenge in cluster-based WSN. The key objective of this research is to develop cluster-based WSN that employs the FSO links to interconnect the CHs to each other. Optimal Cluster Head Localization (OCHL) algorithm is developed to optimally determined the locations of CHs so that the network diversity and coverage area of CHs are improved. Genetic Algorithm (GA) technique is used to obtain a near-optimal solution for the proposed fitness function. Simulation results show that the proposed OCHL algorithm improves the network diversity and coverage area of cluster-based WSN. The weighting parameter of the proposed fitness function can be adjusted to control the effects of covered areas, and link diversity in the fitness function. Additionally, increasing the number of CHs leads to improve the covered area and link diversity. Furthermore, with growing of the number of GA iterations, a better solution for the proposed optimization problem is obtained. Moreover, the Bit Error Rate and Signal to Noise Ratio of FSO links are evaluated based on the rain rate, snow rate, fog, transmitted power, transmitter and receiver aperture diameters, FSO communication range, and weighting parameter. The results demonstrate that the normalized covered area in case of using the proposed OCHL algorithm outperforms as compared to NFCA and LEACH algorithms with 12.95 and 8.52% rise, respectively. In addition, the proposed OCHL algorithm enhances the normalized link diversity by 14.15 and 19.21%, compared with NFCA and LEACH algorithms, respectively.