Review on Energy-Efficient Routing Protocols in WSN

Ram, G. Mohan; Ilavarsan, E.

doi:10.1007/978-981-16-0965-7_66

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…where di portrays the dimension space, q random in ð Þ represents the arbitrary elucidation engaged in the range of 1,B ð Þ at the in th iteration, levy b di ð Þ embodies the levy flight dissemination utility, and it premeditated with the help of subsequent Equation (28),…”

Section: Ideal Path Assortmentmentioning

confidence: 99%

“…Wireless sensor network is combined with many business activities for smart homes, virtual reality, infrastructure, artificial intelligence, machine learning, health care, monitoring, surveillance, decision‐making, and so on 10 . The efficiency of decision‐making is solely dependent on sensor sensitivity 28 . Stakeholders can utilise this network for observations, data gathering, storage, forecasting, forecasting forecasts, and corporate policy making 21 .…”

Section: Introductionmentioning

confidence: 99%

“…10 The efficiency of decision-making is solely dependent on sensor sensitivity. 28 Stakeholders can utilise this network for observations, data gathering, storage, forecasting, forecasting forecasts, and corporate policy making. 21 The observation has been more reliable and correct if the sensor sensitivity is high.…”

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confidence: 99%

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Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

Soundararajan,

Tapas Bapu,

Sargunavathi

et al. 2023

Int J Communication

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SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

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Section: Ideal Path Assortmentmentioning

confidence: 99%