BAFSA: Breeding Artificial Fish Swarm Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Sengottuvelan, P.; Prasath, N.

doi:10.1007/s11277-016-3340-7

Cited by 48 publications

(27 citation statements)

References 13 publications

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“…The number of information and codewords must be chosen so that the number of messages cannot exceed the number of bits used to characterize the information [184], the number of codewords must be less than the capacity used to calculate the external storage space. In [185] the authors proposed an optimized cluster head selection using an improved artificial fish swarm algorithm (AFSA) metaheuristic, the proposed algorithm improved the network performance and lifetime. In [186] the authors proposed a reliable spanning tree construction algorithm, which is called reliable spanning tree construction in IoT (RST-IoT), the algorithm utilized the artificial bee colony algorithm to generate proper trees, the proposed algorithm improved the reliability of data gathering in emergency applications compared to the previous approaches.In [113] the authors proposed a new back propagation (BP) neural network based on an improved shuffled frog leaping algorithm (ISFLA), the ISFLA algorithm was developed on the basis of a chaotic operator and overcome the shortcomings of conventional shuffled frog leaping algorithm (SFLA).…”

Section: ) Error Correction Code Mechanismmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: ) Error Correction Code Mechanismmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…Then, an enhanced artificial fish swarm algorithm-based cluster head selection scheme (EAFSA-CHSS) was proposed as an attempt to concentrate on the extension of network lifetime. 31 This percentage of dead nodes in the network of this EAFSA-CHSS was determined to 13.21% lower than the comparable genetic and PSO algorithms. An ant colony evolutionary algorithm-based cluster head selection scheme (ACEA-CHSS) was proposed for effective clustering process by optimal selection of cluster heads in the network.…”

Section: Related Workmentioning

confidence: 81%

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Murugadass

Poruran

2020

Int J Communication

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Summary Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.

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“…It is not accurate so that the model parameters solved according to the results are not accurate. Therefore, the above steps are repeated again [27].…”

Section: A Principle Of Gmm-em and Anomaly Detectionmentioning

confidence: 99%

Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features

Wang

Zhao³

et al. 2021

IEEE Access

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BAFSA: Breeding Artificial Fish Swarm Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Cited by 48 publications

References 13 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features

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