Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks

Sureshkumar, K.; Vimala, P.

doi:10.1016/j.comnet.2021.108250

Cited by 73 publications

(16 citation statements)

References 34 publications

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“…e node deployment of two scenarios is shown in Figure 3 with BS at the center. A comparative analysis is made with existing methods such as exponentially-ant lion whale optimization algorithm (E-ALWO) [24], Energy-Efficient Scalable Routing Algorithm (EESRA) [25], multidimensional scaling map (MDS-MAP) [26], grey wolf optimization, and Routing Protocol for low power and Lossy networks (RPL) [27].…”

Section: Experimental Validationmentioning

confidence: 99%

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

A-Matarneh

Alqaralleh

Aldhaban

et al. 2022

Computational Intelligence and Neuroscience

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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.

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Section: Experimental Validationmentioning

confidence: 99%

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

A-Matarneh

Alqaralleh

Aldhaban

et al. 2022

Computational Intelligence and Neuroscience

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“…X et al [134] presented a hybrid IWOA, the optimized grey seasonal variation index (IWOA-OGSVI) model, to improve forecasting accuracy and speed. In [135], an energy efficient and trust based routing model was designed using the proposed exponentially ant lion optimization algorithm to route data packets to a receiver. Singh [136] proposed a novel approach based on a WOA for data clustering.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…Kumar and Vimala [20] developed energy and trust based routing by using Exponentially-Ant Lion Whale Optimization (E-ALWO). This E-ALWO was the combination of the exponentially weighted moving average with ant lion and whale optimizations.…”

Section: Related Workmentioning

confidence: 99%

Secure and Energy Aware Cluster based Routing using Trust Centric – Multiobjective Black Widow Optimization for large scale WSN

Chada

Narsimha

2022

Int. j. electr. comput. eng. syst. (Online)

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Wireless Sensor Network (WSN) is a promising approach that is developed for a wide range of applications due to its low installation cost. However, the nodes in the WSN are susceptible to different security threats, because these nodes are located in hostile or harsh environments. Moreover, an inappropriate selection of routing path affects the data delivery of the WSN. The important goal of this paper is to obtain secure data transmission while minimizing energy consumption. In this paper, Trust Centric - Multiobjective Black Widow Optimization (TC-MBWO) is proposed for selection of Secure Cluster Head (SCH) from the large-scale WSN. Moreover, the secure routing path is generated by using the TC-MBWO, in which the factors considered for the cost function are: residual energy, distance, trust and node degree. Therefore, the secured clustering and routing achieved by using TC-MBWO, provides the resistance against malicious nodes and simultaneously the energy consumption is also minimized by identifying the shortest path. The proposed TC-MBWO method is analyzed in terms of alive nodes, dead nodes, energy consumption, throughput, and network lifetime. Here, the TC-MBWO method is compared with different existing methods such as Low Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm Optimization - Grey Wolf Optimizer (PSO-GWO), Particle-Water Wave Optimization (P-WWO) and Particle-based Spider Monkey Optimization (P-SMO). The alive nodes of the TC-MBWO are 70 for 2800 rounds which are higher in number when compared to the PSO-GWO, P-WWO and P-SMO.

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Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks

Cited by 73 publications

References 34 publications

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

A survey on several new popular swarm intelligence optimization algorithms

Secure and Energy Aware Cluster based Routing using Trust Centric – Multiobjective Black Widow Optimization for large scale WSN

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