An Enhanced Multi-Objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks
In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resou...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/d6788be180e145bfa1c187fe93de6519 |
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| Sumario: | In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resource (battery), which requires considerable importance on deployment and routing strategies, to improve the performance of Quality of Service (QoS) in WSNs. Many of the existing strategies are based on metaheuristics algorithms such as Genetic Algorithms to resolve the problem. This research proposes a new algorithm, Enhanced Non-Dominated Sorting Genetic Routing Algorithm (ENSGRA), to improve the QoS in WSNs. The proposed algorithm relies on Non-Dominated Sorting Genetic Algorithm 3 (NSGA-III), but adjusts reference points through the use of a dynamic weighted clustered scheduled vector to obtain new solutions. Moreover, ENSGRA can be used to find an integration between two parents crossover with multi-parent crossover (MPX), to produce multiple children and improve new offspring to obtain the optimal Pareto Fronts (PF). This algorithm excels when compared with the lagged multi-objective jumping particle swarm optimization, Non-dominated Sorting Genetic Algorithm–II and NSGA-III in terms of the QoS model (31% optimization percentage). Results show that the proposed ENSGRA is superior over other algorithms in evaluation measures for multi-objective algorithms. |
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