Antipredator Adaptation Shuffled Frog Leap Algorithm to Improve Network Life Time in Wireless Sensor Network

Anandamurugan, S.; Abirami, T.

doi:10.1007/s11277-016-3354-1

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…In [197] the authors proposed a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address the problem of unstable topological structure, and utilized the AFSA algorithm to perform the initial route discovery in order to find feasible routes quickly. In [198] the authors proposed a hybrid shuffled frog leaping algorithm (AASFLA) with antipredator capabilities to avoid the local minima, improved the performance and lifetime for MWSNs. In [199] the authors proposed an evolutionary multi-objective optimization approach based on nondominated sorting genetic algorithm-II (NSGA-II), the proposed algorithm can improve the network lifetime and coverage while maintaining the network connectivity.…”

Section: F Topology Optimizationmentioning

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: F Topology Optimizationmentioning

confidence: 99%

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

2019

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“…In this paper, shuffled frog leading algorithm [ 26 ] is used to optimize the weight factor. Initialize the weight factor as frog swarm, and then perform shuffled frog leading algorithm to solve the weight factor value.…”

Section: Music Mooc Resource Recommendation Based On Mixed Collaborat...mentioning

confidence: 99%

Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

Wang

2022

Computational Intelligence and Neuroscience

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With the rapid development of MOOC platforms, MOOC resources have grown substantially, causing the problem of information overload. It is difficult for users to select the courses they need from a large number of MOOC resources. It is necessary to help users select the right music courses and at the same time make the outstanding music courses stand out. Recommendation systems are considered a more efficient way to solve the information overload problem. To improve the accuracy of the recommendation results of music MOOC resources, a mixed collaborative filtering recommendation algorithm based on Spark architecture is proposed. First, the user data and item data are modeled and scored by the collaborative filtering algorithm, then the tree structure of the XGBoost model and the features of regular learning are combined to predict the scores, and then the two algorithms are mixed to solve the optimal objective function to obtain the set of candidate recommendation data. Then, the frog-jumping algorithm is used to train the weighting factors, and the optimal combination of weighting factors is used as the training result of the samples to realize the data analysis of the mixed collaborative filtering recommendation algorithm. The experimental results in the music MOOC resource show that the average absolute error and root mean square error of the proposed method are 0.406 and 1.117, respectively, when the sparsity is 30%, which are lower than those of other existing collaborative filtering recommendation methods, with higher accuracy and execution efficiency.

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“…In the imitating progress step, information is swapped among the frogs in a memeplex [18]. The frogs are shambled post a clear figure of imitating progress step for enhanced standard of memeplex.…”

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

An Fuzzy Trust Based Energy Aware Multipath Secure Data Collection in WSN

Babu¹,

Anuradha²,

Murty³

2018

IJET

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A Wireless Sensor Network (WSN) is an array of radar nodules that energetically organize itself into a cable-less grid without utilizing any prevailing arrangement. One of the significant issues in WSNs is the drive's intake, whereby the grid's lifespan is reliant on this aspect. Security is another significant issues in cable-less radar grids, because of absence of wire/cable besides resource constraint's nature. Reliance model shows a key role in fortifying the radar grids by recognizing the egotistical, malevolent and give -in nodules and separating them from message grid. Directed Random Propagation (DRP) operates two-hop vicinity data to enhance the circulation competence, leading to a reduced pack capture possibility. In this study, Shuffled Frog Leaping Algorithm (SFLA) is applied to augment ill-defined faith centered drive conscious multiple route locked data group in WSN.

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Antipredator Adaptation Shuffled Frog Leap Algorithm to Improve Network Life Time in Wireless Sensor Network

Cited by 14 publications

References 23 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

Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

An Fuzzy Trust Based Energy Aware Multipath Secure Data Collection in WSN

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