Localized Distributed Secure Fast Neural Routing for QoS Maximization in WSN with IoT Using Machine Learning
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 11 |
| Year of Publication : 2023 |
| Authors : N. Babu, Tamilarasi Suresh |
10.14445/23488549/IJECE-V10I11P104
How to Cite?
N. Babu, Tamilarasi Suresh, "Localized Distributed Secure Fast Neural Routing for QoS Maximization in WSN with IoT Using Machine Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 11, pp. 33-44, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I11P104
Abstract:
Several routing algorithms in the literature promote secure communication between nodes of Wireless Sensor Networks (WSN). Most approaches perform route selection according to behaviour, frequency of transmission, throughput, latency, and other factors of any route. However, the deficiency of transmission details challenges the routing protocol and leads to poor security performance. By considering this, an efficient Localized Distributed Secure Fast Neural Routing (LDSFNR) is presented in this paper. The method focused on performing route selection according to the partial route trust, which is computed in a distributed way with a localized structure. Even though the nodes can choose their forwarder, the protocol is restricted for the Sensor Nodes (SN) and does not provide any freedom for Internet of Things (IoT) devices. With this result, the SNs involved in transmissions keep track of transmission traces in both ways. Using the traces, the intermediate node would measure the trust value of the partial route up to k hops. To calculate the trust value, the intermediate node would train the Neural Network (NN) with the available traces. The neurons are designed to measure the Partial Trust Score (PTS) for the k hop, which is calculated based on behaviour, transmission rate, retransmission rate, latency, etc. The model initially finds the set of routes to attain the target node and computes the Complete Trust Score (CTS) for various routes in the fast neural testing. Based on the result of neural testing, an optimal way is selected and transmitted. The intermediate node applies localized fast neural routing, identifying a secure route and forwarding the data packet according to the traces. The proposed model enhances the secure routing performance in WSN with higher Quality of Service (QoS) performance.
Keywords:
WSN, IoT Devices, Secure routing, QoS maximization, LDSFNR.
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