Affiliations:
Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu
Correspondence:
[*]
Corresponding author. P. Parthiban, Assistant Professor, Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu. E-mail [email protected].
Abstract: Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4.
Keywords: Four point curve cryptographic and rivest shamir adleman (FPCCRSA), Haversine with weighted function based fuzzy c-means clustering (HWF-FCM), wireless sensor network, Cluster head (CH), sigmoid deep long short term memory (SDLSTM)
