Clustering for Energy Efficient and Redundancy Optimization in WSN using Fuzzy Logic and Genetic Methodologies a Review

Saadaldeen, Razan S. M.; Osman, Abdalla A.; Ahmed, Yousif Elhadi Elsideeg

doi:10.1109/iccceee.2018.8515880

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Formulation of the optimization task of a particular application optimizes the result managed by a linear or a non-linear encoding methodology. The metaheuristic techniques recognize with stimulations of biological progression, e.g., Genetic Algorithm (GA) [160][161][162], by the procedure of usual collection, the Ant Colony Optimization algorithm (ACO) [158]. The optimum solution is encouraged by the research and development procedures.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

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Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

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Section: Metaheuristic Methodsmentioning

confidence: 99%

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

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“…There are some parameters like Fault Tolerance (Mohapatra, & Mohapatra, 2019), Power Consumption (Ebrahimi, & Tabatabaei, 2020), Data Aggregation (Babu et al, 2020, Quality of Service (Yagouta, Jabberi, & Gouissem, 2018), Data Latency (Hidoussi et al, 2017), Load Balancing, execution time (Edla, Kongara, & Cheruku, 2019 and Node Deployment (El Khediri et al, 2020) with different techniques like Fuzzy Logic (Saadaldeen, Osman, & Ahmed, 2018), K-means++ and Fuzzy Logic (Wen et al, 2019), and also Hybrid Clustering (Malshetty, & Mathapati, 2019) that must be considered while implementing the clustering protocols. At 800 nodes, energy consumption is 40%; for 600 nodes, it is 30%.…”

Section: Related Workmentioning

confidence: 99%

HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

Maheshwar

Veenadhari

2023

JCST

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Wireless sensor network (WSN) is composed of multiple sensors that are connected through a communication channel and communicate with each other. As these sensor nodes are battery-operated, therefore, as a consequence, battery life or energy is always an issue of concern. Therefore, researchers focus their work on optimizing the routing strategies to save energy wastage in WSNs. Among all routing strategies, cluster-based techniques proved to be quite able to successfully manage propagation from sender to receiver. Because it must gather all data and send it to the base station, each cluster's elected head is responsible for bearing the complete load. A cluster-based routing mechanism is established under this paper and termed a Heterogeneous Cluster Prediction and Formation Routing Protocol (HCPFR) in which the algorithm first creates the cluster and predicts the energy utilization or network lifetime, and then provides energy-efficient optimized clustering. In this method, the proposed HCPFR model is compared with different methods; LEACH PSO, LEACH-GWO, LEACH-EEGWO, and FZR, and the performance is compared with different parameters mainly First Dead Node (FDN), Network Longevity (NL) and throughput (THP) in term of packet delivered and residual energy. The result shows that the HCPFR model outperforms better over these approaches. The FDN, NL, and THP of the proposed HCPFR are nearly 8000,10000, and 30000. Also, the suggested model shows that as the number of rounds increases the residual energy drops to 0.1 from 3.8 as the rounds increases to 10000 from 2000.

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“…biology [20]. The evolutionary algorithms, however, have already shown their specific capabilities in optimizing problems relating to the parameter evaluation [21,22]. Genetic algorithms are generally applied to generate high-quality solutions to where the variables to be optimized (genes) can be encoded to form a string (chromosome) [23].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Distance Mapping Algorithm for Sensor Node Localization in WSNs

Tan

Shao

et al. 2019

Int J Wireless Inf Networks

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The localization of sensing nodes is most pronounced in the application of wireless sensor networks. To address this issue, a node localization algorithm called the DMA is proposed in this paper. This algorithm identifies the node position by using the estimation matrix and distance matrix together with the optimized linear transforming function. With the integration of GA, the position of the node can be accurately determined. The conducted simulation outcomes and the corresponding analysis verify the high accuracy and low energy consumption of the proposed algorithm, which can outperform other widely used approaches. This study designs and deploys the proposed algorithm and shows its sensor node localization theory, which makes it a promising basis for the realization of positioning in WSNs.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Clustering for Energy Efficient and Redundancy Optimization in WSN using Fuzzy Logic and Genetic Methodologies a Review

Cited by 5 publications

References 11 publications

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

Distance Mapping Algorithm for Sensor Node Localization in WSNs

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