Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

Mihoubi, Miloud; Rahmoun, Abdellatif; Lorenz, Pascal

doi:10.4018/ijdst.2019010106

Cited by 14 publications

(5 citation statements)

References 35 publications

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“…To the present author's knowledge, most of the literatures on applications of WOA in WSN localization use range-based techniques [28][29][30][31][32]. The experimental results show that the proposed schemes achieve better localization accuracy, delivery radio, and delay.…”

Section: Related Workmentioning

confidence: 89%

“…In response to these shortcomings, researchers have improved WOA, such as chaotic WOA [18,19], improved WOA [20,21], binary WOA [22,23], hybrid WOA [24,25], and multi-objective WOA [26,27]. In recent years, WOA has been employed in range-based node localization algorithms [28][29][30][31][32], but few applications in DV-Hop. For instance, Chai et al [33] only utilized parallel techniques to improve WOA and applied it to enhance the performance of DV-Hop, but they do not take into account the memory consumption and storage capacity of the node.…”

Section: Introductionmentioning

confidence: 99%

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Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Wang

et al. 2022

Wireless Netw

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Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

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Section: Related Workmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Wang

et al. 2022

Wireless Netw

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“…In 2019, a new technique called moth-flame optimization algorithm (MFOA) has been suggested to overcome with the node localization problem. 31 Moreover, the moth moves effectively via different trajectories, like the spiral trajectory. The simulation results indicate the efficiency of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

A hybrid Harrison Hawk optimization based on differential evolution for the node localization problem in IoT networks

Baidar

Rahmoun²,

Mihoubi

et al. 2022

Int J Communication

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Summary Despite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.

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“…Moth Flame Optimization Algorithm (MFOA) is the latest approach proposed to deal with node localization in distributed paradigm of WSN [22], MFOA it is show are combination between PSO and genetic algorithm (GA) , furthermore, the moth fly smartly through several trajectory as spiral trajectory, the simulations result prove that MFOA is very efficient and improve the performance of the network .…”

Section: Fig 1 Flip Ambiguity Phenomenonmentioning

confidence: 99%

Whale Optimization Approach for Optimization Problem In Distributed Wireless Sensor Network

Baidar

Rahmoun

Lorenz

et al. 2019

Proceedings of the 9th International Conference on Information Systems and Technologies

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Recently, Node localization has played a vital role in wireless sensor networks (WSN), where the node's coordinates have mostly required before sensing operation. Sensor devices are randomly deployed in the network for monitoring task. The crucial objective of localization is to determine the position of the sensor nodes based on a few landmark nodes. Node localization problem is formulated as an optimization problem and metaheuristic techniques are called to deal with the issue in multidimensional space, in this paper, the Whale Optimization Algorithm (WOA) is proposed to deal with the challenge, furthermore, the capability of auto localization is highly requested. WOA computes iteratively the node's positions based on a multistage approach to browse the whole region of the network. Deploying this algorithm on a large WSN with hundreds of sensors shows good performance in terms of node localization. the simulation result proved the good performance of WOA in term of localization error and computing time . CCS CONCEPTSCCS → Networks → Network types → Wireless access networks.

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Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

Cited by 14 publications

References 35 publications

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

A hybrid Harrison Hawk optimization based on differential evolution for the node localization problem in IoT networks

Whale Optimization Approach for Optimization Problem In Distributed Wireless Sensor Network

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