Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

M. Sindhuja

doi:10.1016/j.adhoc.2022.103037

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

2023-03-01

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DOI：10.1016/j.adhoc.2022.103037

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Abstract

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Wireless sensor network (WSN) combines and transmit the data in internet of things (IoT) applications. The nodes in pervasive Wireless sensor network are battery-operated and it needed to develop an energy-efficient approach, which lessens the energy consumption and increases the network lifetime. The existing methods not provide effective cluster head (CH) selection and node computation.In this manuscript, Multi-Objective Cluster Head Based Energy Aware Routing using Self-attention based progressive generative adversarial network optimized with African vulture optimization is proposed for Secured Data aggregation in WSN (MOCHSAPGAN-AVO). Initially, the proposed model performs the routing process through Cluster heads (CH). So, the data aggregation using Self-attention based progressive generative adversarial network selects CH based on the multi-objective fitness function. These multi-objective fitness functions consider factors, like energy, delay, throughput, distance between the nodes and cluster density. After the cluster head selection, the optimal path is used to transfer the data to the base station. The optimal path is based on three parameters, such as degree of satisfication, connectivity and Rate of service (RoS). Hence, these three parameters are optimized with the help of African vulture optimization approach for optimal path selection. Finally, the optimal path is used for transferring the aggregated data to the base station (BS) and vice versa. The proposed method is implemented in network simulator (NS2) tool and the performance of proposed method is estimated. The proposed method attains 30.39%, 34.64%, 45.36%, and 33.32% lower delay, 39.056%, 44.12% and 28.97% improved packet delivery ratio and 45.96%, 48.06%, and 38.16% lower packet drop compared with existing methods, like hybrid and energy-efficient secure data aggregation algorithm and fuzzy logic based secure data aggregation in wireless sensor network (FL-CHESDA-WSN), multi objective fostered male lion optimization algorithm (DA-WSN-MLOA) and an Adaptive Source Location Privacy Preservation Technique and Principle Component Analysis (IDAFASLPP-RR).

Keywords:

African vulture optimization

Cluster head selection

Clustering process

Multi -objective fitness function

Self -attention based progressive generative

adversarial network

Secured data aggregation

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

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