Distributed Artificial Intelligence Based Cluster Head Power Allocation in Cognitive Radio Sensor Networks

Mukherjee, Amrit; Goswami, Pratik; Yang, Lixia

doi:10.1109/lsens.2019.2933908

Cited by 18 publications

(13 citation statements)

References 12 publications

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“…The proposed SabGAN-AqOA-EgAwR-WSN model provide 20.92%, 21.01%, 19.32%, 18.32%, 20.04%, 19.43%, 20.43%, 18.56%, 20.84%, 17.06%, 16.34%, and 15.40% lower delay compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al, 35 Srividya and Devi, 33 Mukherjee et al, 24 Mukherjee et al, 23 Mukherjee et al, 22 Jain et al, 11 Verma and Jha, 34 Sindhuja et al, 32 Saranraj et al, 30 and Mehta and Saxena, 20 respectively. The proposed SabGAN-AqOA-EgAwR-WSN model provide 22.02%, 21.81%, 20.62%, 19.72%, 20.04%, 17.43%, 19.03%, 18.26%, 19.04%, 17.96%, 16.14%, and 14.70% lower energy consumption compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al, 35 Srividya and Devi, 33 Mukherjee et al, 24 Mukherjee et al, 23 Mukherjee et al, 22 Jain et al, 11 Verma and Jha, 34 Sindhuja et al, 32 delivery ratio compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al, 35 Srividya and Devi, 33 Mukherjee et al, 24 Mukherjee et al, 23 Mukherjee et al, 22 Jain et al, 11 Verma and Jha, 34 Sindhuja et al, 32 Saranraj et al, 30 and Mehta and Saxena, 20 respectively. The proposed SabGAN-AqOA-EgAwR-WSN model provides 13.07%, 14.36%, 16.67%, 12.45%, 14.87%, 17.43%, 19.34%, 20.75%, 22.74%, 21.75%, 20.65%, and 24.32% higher network lifetime compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al,…”

Section: Simulation Resultsmentioning

confidence: 92%

“…Trust: it is generally defined as the trust among the node. The trust constraint is premeditated based on the subsequent Equation (22),…”

Section: Ideal Path Assortmentmentioning

confidence: 99%

“…23 Therefore, the self-attention based generative adversarial network (SabGAN) come near to use in this work for optimize the power complementary between the sensor nodes and the path creation among the cluster head with BS. 22 Objectives like proximity, residual network, coverage, communication cost has considered to cluster head selection and node clustering. The Aquila optimization algorithm (AqOA) method has been used for better path from CH to BS used for transferring information.…”

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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: Simulation Resultsmentioning

confidence: 92%

“…Trust: it is generally defined as the trust among the node. The trust constraint is premeditated based on the subsequent Equation (22),…”

Section: Ideal Path Assortmentmentioning

confidence: 99%

mentioning

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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“…Because the agent is responsible for data collection, fusion and distribution in the network, an accurate location information and response time of the agent will affect the delay and work efficiency of the entire network [19]. Reference [20] adopted an agent-based Fuzzy Group Optimization algorithm (FGO) to reduce energy consumption and prolonged the life cycle of nodes in the WSNs. In [21], authors reduce the number of sensors to be selected using Multiplayer Perceptron (MLP), Support Vector Machine (SVM) and Naïve Bayes for extending WSN lifetime.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

et al. 2021

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“…For the current time t, the battery power consumption and memory usage in the network can be expressed as [34]:…”

Section: A Inter-cluster Resource Allocationmentioning

confidence: 99%

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

et al. 2019

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Resource allocation in the Internet of Things (IoT) applications for Wireless Sensor Networks (WSNs) is a challenging problem that requires tasks processing from the appropriate sensor nodes without compromising the Quality-of-Service (QoS). Due to heterogeneity in sensors, the inter-cluster and intra-cluster cooperative communication between sensor nodes hinders the overall resource allocation of the network in terms of energy consumption and response time. Therefore, this paper establishes a multi-agent clustering WSN model, i.e., Adaptive Distributed Artificial Intelligence (ADAI) technique with a hierarchical resource allocation strategy to address the issue of resource allocation in these types of network. For the inter-cluster power allocation, we are considering QoS and energy consumption factors with DAI. Moreover, for intra-cluster resource allocation, this paper introduces Adaptive Particle Swarm Optimization (APSO), which uses its objective functions as the node distance and respective energy loads. The mathematical analysis and simulation results validate the propose method in terms of energy consumption and response time of the network.

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Distributed Artificial Intelligence Based Cluster Head Power Allocation in Cognitive Radio Sensor Networks

Cited by 18 publications

References 12 publications

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

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

An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

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