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
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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.

References:

[1] Wassim Jerbi et al., “A Novel Blockchain Secure to Routing Protocol in WSN,” 2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR), pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Mallanagouda Biradar, and Basavaraj Mathapathi, “Secure, Reliable and Energy Efficient Routing in WSNs: A Systematic Literature Survey,” 2021 International Conferences on Advance in Electrical, Computing, Communications and Sustainable Technologies (ICAECT), pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Puja Rani, and Neetesh Kumar Gupta, “Composite Trusts for Secured Routing Strategy through Energy-Based Clustering in WSNs,” 2021 International Conferences on Advance in Electrical, Computing, Communications and Sustainable Technologies (ICAECT), pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Majid Alotaibi, “Improved Blowfish Algorithms-Based Secure Routing Techniques in IoT-Based WSN,” IEEE Access, vol. 9, pp. 159187-159197, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] M. Manisha Rathee et al., “Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks,” IEEE Transaction on Engineering Managements, vol. 68, no. 1, pp. 170-182, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] C. Senthilkumar et al., “Experimental Analysis of Secure Routing Protocols Establishment over Wireless Sensors Networks,” 2021 5th International Conferences on Trend in Electronic and Informatics (ICOEI), pp. 691-698, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Muhammad K. Khan et al., “Hierarchical Routing Protocols for Wireless Sensor Networks: Functional and Performance Analysis,” Journal of Sensors, vol. 2021, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Huangshui Hu et al., “Trusts Based Secured and Energy Efficient Routing Protocols for Wireless Sensors Network,” IEEE Access, vol. 10, pp. 10585-10596, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Weidong Fang et al., “MSCR: Multidimensional Secure Clustered Routing Scheme in Hierarchical Wireless Sensors Network,” EURASIP Journal on Wireless Communication and Networking, vol. 14, pp. 1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Huangshui Hu, “Trust-Aware Secured Routing Protocols for Wireless Sensors Network,” ETRI Journal, vol. 43, no. 4, pp. 674-683, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] M. Saud Khan et al., “A Low-Complexity, Energy-Efficiency Data Secure Models for Wireless Sensors Networks Based on Linearly Complex Voices Encryption Mechanisms of GSM Technology,” International Journal of Distributed Sensor Networks, vol. 17, no. 5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Pradeep Sadashiv Khot, and Udaykumar Naik, “Particle-Water Wave Optimizations for Secured Routing in Wireless Sensors Networks Using Cluster Heads Selections,” Wireless Personal Communication, vol. 19, pp. 2405-2429, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Qingzeng Xu, “Wireless Sensors Network Secured Routing Algorithms Based on Trust Values Computations,” International Journal of Internet Protocol Technology, vol. 14, no. 1, pp. 10-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] S. Sudha Mercy, J.M. Mathana, and J.S. Leena Jasmine, “An Energy-Efficient Optimal Multi-Dimensional Locations, Keys and Trust Managements Based Secured Routing Protocols for Wireless Sensors Network,” KSII Transaction on Internet and Information System, vol. 15, no. 10, pp. 3834-3857, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] L. Rajesh, and H.S. Mohan, “A Multilevel Efficient Energy Clustering Protocols with Secured Routing (MEECSR) in WSN,” International Journal of Applied Science and Engineering, vol. 18, no. 2, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] T.G. Ganga, and R.A. Roseline, “A Review on Secured and Energy-Efficient Routing Protocol in Wireless Sensors Network (WSNs),” International Journal of Engineering Research and Technology, vol. 10, no. 3, pp. 196-202, 2021.
[Google Scholar] [Publisher Link]
[17] Pradeep Sadashiv Khot, and Udaykumar Laxman Naik, “Cellular Automata-Based Optimized Routing for Secured Data Transmissions in Wireless Sensors Network,” Journal of Experimental and Theoretical Artificial Intelligences, vol. 34, no. 3, pp. 431-449, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Xueli Wang, “Low-Energy Secured Routing Protocols for WSN Based on Multi-Objective Ant Colony Optimization Algorithms,” Journal of Sensor, vol. 2021, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Oladayo Olufemi Olakanmi, “A Lightweight Security and Privacy-Aware Routing Scheme for Energy-Constraints Multi-Hop Wireless Sensors Network,” International Journal of Information and Computers Security, vol. 15, no. 2, pp. 231-253, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Tayyab Khan, and Karan Singh, “TASRP: A Trust Aware Secured Routing Protocols for Wireless Sensors Network,” International Journal of Innovative Computing and Application, vol. 12, no. 2, pp. 108-122, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Aditya Pathak, Irfan Al-Anbagi, and Howard J. Hamilton, “An Adaptive QoS and Trusts-Based Lightweights Secured Routing Algorithms for WSNs,” IEEE Internet of Thing Journal, vol. 9, no. 23, pp. 23826-23840, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sandeep Verma et al., “Intelligent and Secured Clustering in Wireless Sensors Networks (WSN)-Based Intelligent Transportations System,” IEEE Transaction on Intelligent Transportations System, vol. 23, no. 8, pp. 13473-13481, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Adeel Ahmed et al., “An Energy-Efficient Data Aggregation Mechanisms for IoT Secured by Blockchains,” IEEE Access, vol. 10, pp. 11404-11419, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Manaf Bin-Yahya, Omar Alhussein, and Xuemin Shen, “Securing Software-Defined WSNs Communications via Trusts Managements,” IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22230-22245, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Muhammad Nouman et al., “Malicious Nodes Detections Using Machine Learning and Distributed Data Storages Using Blockchains in WSN,” IEEE Access, vol. 11, pp. 6106-6121, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] C. Narmatha, “A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs,” Journal of Computational Science and Intelligent Technologies, vol. 1, no. 3, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]