Investigations on IOT-Based WSN with SWIPT-NOMA Combination
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 6 |
| Year of Publication : 2023 |
| Authors : Reginald Jude Sixtus, Tamilarasi Muthu |
10.14445/23488549/IJECE-V10I6P109
How to Cite?
Reginald Jude Sixtus, Tamilarasi Muthu, "Investigations on IOT-Based WSN with SWIPT-NOMA Combination," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 6, pp. 104-118, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I6P109
Abstract:
Wireless Communication provides the interconnection of different devices for the ubiquitous accessibility of intellectual capacity. Wireless Communication incorporates device interaction to provide sufficient capability in networking between intermediate devices. Conventionally, the Internet of Things (IoT) and Wireless Sensor Networks (WSN) offer sufficient information between intermediate devices. IoT-WSN devices are resource constraints (RC), compact devices, and limited battery resources. The increase in the number of users leads to challenges with security in the IoT-WSN. The data transmission between the wireless communication uses the 5G communication-based NOMA communication. Due to limited RC features, the computational complexity is higher with minimal space consumption; those are evaluated with embedded hardware features within the IoT – WSN. This paper aimed to develop an appropriate optimal routing scheduling and security model based on Long Short-Term Memory (LSTM). The performance of the proposed ORSS is evaluated for security analysis based on consideration of different attacks. With the ORSS model, the position of nodes is computed with the covariance matrix estimation. To identify the optimal route, 's Particle Swarm Optimization (PSO) algorithm is implemented for the route scheduling for the data transmission. SWIPT is implemented for effective energy harvesting to minimize energy consumption within the network. Based on the covariance estimation, optimal routes in the network are computed to detect attacks. The attacks are computed based on the utilization of the LSTM model for the detection and classification of attacks with the use of CICIDS datasets. The comparative analysis stated that the proposed ORSS exhibits ~40% higher data transmission and ~21% reduced delay than state-of-techniques
Keywords:
Internet of Things (IoT), Simultaneous Wireless Information and Power Transfer (SWIPT), Particle Swarm Optimization (PSO), Long Short Term Memory (LSTM), Non-Orthogonal Multiple Access (NOMA), Routing, Attacks.
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