CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications

Shende, Dipali K.; Sonavane, S. S.

doi:10.1007/s11276-020-02299-y

“…The proposed approach is analyzed using the network simulator ns-3 and compare the simulation results with the other existing approaches like B-iHTRP [21], HyphaNet [28], QNewBee [29], and Crowwhale-ETR [30]. In the simulation environment, 100 nodes are consistently distributed in the area of 1200 x 1200 sq.m.…”

Section: Resultsmentioning

confidence: 99%

ANFIS Based Optimal Routing using Group Teaching and Adaptive Equilibrium Optimization based Trust Aware Routing Protocol in MANET

Hemalatha

¹

,

Umamaheswari²,

Jothi

³

2021

Preprint

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Recently, routing is considered the main problem in MANET due to its dynamic nature. The route discovery and the optimal route selection from the multiple routes are established for the efficient routing in MANET. The major objective of this research is to select the optimal route for packet transmission in MANET. In this paper, four stages namely trust evaluation, route discovery, optmal route selection and route maintanance are elucidated. Initially, the trust evaluation is made by using ANFIS where the primary trust values are evaluated. The next stage is the route discovery scheme, in which the routes are established by Group teaching optimization algorithm (GTA). From the route discovery scheme, multiple routes are found. The optimal route for the transmission is selected with the help of the Adaptive equilibrium optimizer (AO) algorithm. Finally, the route maintenance process is established; if any of the routes fails for the broadcast it immediately selects the alternate optimal route from the multi-zone routing table for efficient packet transmission. The proposed approach is evaluated by various performance measures like throughput, energy consumption, packet delivery ratio, end-to-end delay, packet loss rate, detection rate, and routing overhead. This result describes that the proposed approach outperforms other state-of-art approaches.

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“…The proposed approach is analyzed using the network simulator ns-3 and compare the simulation results with the other existing approaches like B-iHTRP (Çaydaş et al 2009), HyphaNet (Costa et al 2020), QNewBee (Labed et al 2019), and Crowwhale-ETR (Shende and Sonavane, 2020). In the simulation environment, 100 nodes are consistently distributed in the area of 1200 x 1200 sq.m.…”

Section: Resultsmentioning

confidence: 99%

Optimal Route Maintenance based on Adaptive Equilibrium Optimization and GTA based Route Discovery Model in MANET

Hemalatha

¹

,

Umamaheswari

²

,

Jothi

³

2021

Preprint

0

View full text Add to dashboard Cite

Recently, routing is considered the main problem in MANET due to its dynamic nature. The route discovery and the optimal route selection from the multiple routes are established for the efficient routing in MANET. The major objective of this research is to select the optimal route for packet transmission in MANET. In this paper, four stages namely trust evaluation, route discovery, optmal route selection and route maintanance are elucidated. Initially, the trust evaluation is made by using ANFIS where the primary trust values are evaluated. The next stage is the route discovery scheme, in which the routes are established by Group teaching optimization algorithm (GTA). From the route discovery scheme, multiple routes are found. The optimal route for the transmission is selected with the help of the Adaptive equilibrium optimizer (AO) algorithm. Finally, the route maintenance process is established; if any of the routes fails for the broadcast it immediately selects the alternate optimal route from the multi-zone routing table for efficient packet transmission. The proposed approach is evaluated by various performance measures like throughput, energy consumption, packet delivery ratio, end-to-end delay, packet loss rate, detection rate, and routing overhead. This result describes that the proposed approach outperforms other state-of-art approaches.

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“…Also, Nawaz-Enscore-Ham (NEH) strategy was used to generate CSA population [127]. Also, in [172] the authors developed a hybrid algorithm that combined WOA with CSA called CrowWhale to solve energy trust routing (ETR). In [173], Farh et al introduced (CSA-PSO), which hybridized CSA with PSO.…”

Section: B Hybrid Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

Hussien

¹

,

Amin

²

,

Wang

³

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications

Cited by 58 publications

References 30 publications

ANFIS Based Optimal Routing using Group Teaching and Adaptive Equilibrium Optimization based Trust Aware Routing Protocol in MANET

ANFIS Based Optimal Routing using Group Teaching and Adaptive Equilibrium Optimization based Trust Aware Routing Protocol in MANET

Optimal Route Maintenance based on Adaptive Equilibrium Optimization and GTA based Route Discovery Model in MANET

Crow Search Algorithm: Theory, Recent Advances, and Applications

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