An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs

Edla, Damodar Reddy; Lipare, Amruta; Cheruku, Ramalingaswamy; Kuppili, Venkatanareshbabu

doi:10.1109/jsen.2017.2750696

Cited by 48 publications

(29 citation statements)

References 18 publications

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“…To minimize the energy consumption on the traveling of the mobile robot, In [149] the authors applied the concept of artificial bee colony (ABC) and design an ABC-based path planning algorithm, and validated the correctness and high efficiency of our proposal. In order to address the energy consumption problem, in [150] the authors proposed the shuffled frog leaping algorithm (SFLA) by suitably modifying the frog's population generation and off-spring generation phases in SFLA and by introducing a transfer phase. The experimental results are encouraging and demonstrated the efficiency of the proposed algorithm.…”

Section: B Data Collectionmentioning

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: B Data Collectionmentioning

confidence: 99%

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

2019

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“…By shortening the communication distance, the energy consumption of cluster heads is reduced, and the network performance is effectively improved. In [27], an improved hybrid leapfrog algorithm is used to select the optimal cluster heads, and considering the residual energy and the density of nodes, an efficient fitness function is designed to evaluate the quality of the solution produced by the algorithm. This algorithm effectively balances the load of cluster heads in the network, thereby extending the network's lifetime.…”

Section: Related Workmentioning

confidence: 99%

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols. INDEX TERMS WSN, clustering, energy efficiency, network lifetime, high throughput, polling, routing algorithm, artificial bee colony

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“…Li et al [27] put forward a hybrid optimal method based on SFLA and bacterial foraging, which has solved the problem of the reliability of the system and redundancy assignment. Edla et al [28] [30] proposed an adaptive hybrid mutation SFLA. However, a few disadvantages, including the high randomness in previous partial search, weakness in global search, and the slow convergence, still exist in the method.…”

Section: Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

An energy‐efficient non‐uniform clustering routing protocol based on improved shuffled frog leaping algorithm for wireless sensor networks

Chu-bin

Yijiahe

et al. 2021

IET Communications

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Wireless sensor networks have been used widely in environmental tracking and monitoring. Nevertheless, WSNs are facing a number of challenges, such as unreasonable cluster head selection and energy‐hole problems while nodes have unbalanced energy consumption. Thus, the paper aims to propose an energy‐efficient non‐uniform clustering routing protocol to enhance nodes energy efficiency and balance the energy consumption in WSNs. In addition, a non‐uniform clustering network partition is introduce to reduce the probability of energy hole occurrences and optimize cluster heads dynamical selection method to suggest an improved shuffled frog leaping algorithm. Ultimately, the simulation experiment has demonstrated a 20% gain of energy efficiency to extend network lifetime by the proposed protocol and enhanced algorithm.

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An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs

Cited by 48 publications

References 18 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

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

An energy‐efficient non‐uniform clustering routing protocol based on improved shuffled frog leaping algorithm for wireless sensor networks

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