A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

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S. Venkatramulu
V. Chandra Shekar Rao
K. Vinay Kumar
C. Srinivas
B. Raghuram
Shaik Rasool


Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation.

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How to Cite
Venkatramulu, S. ., Rao, V. C. S. ., Vinay Kumar, K. ., Srinivas, C. ., Raghuram, B. ., & Rasool, S. . (2023). A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 550–562. https://doi.org/10.17762/ijritcc.v11i7s.7034


Srilakshmi, A., Rakkini, J., Sekar, K. R., & Manikandan, R. (2018). A comparative study on Mobile Ad-hoc Network (MANET) and its applications in smart agriculture. Pharmacognosy Journal, 10(2).

Samie, F., Bauer, L., & Henkel, J. (2019). From cloud down to things: An overview of machine learning in Mobile Ad-hoc Network. IEEE Mobile Ad-hoc Network Journal, 6(3), 4921-4934.

Shadroo, S., & Rahmani, A. M. (2018). Systematic survey of big data and data mining in Mobile Ad-hoc Network. Computer Networks, 139, 19-47.

Accettura, N., Grieco, L. A., Boggia, G., & Camarda, P. (2011, April). Performance analysis of the RPL routing protocol. In 2011 IEEE International Conference on Mechatronics (pp. 767-772). IEEE.

Dr. S. Praveen Chakkravarthy. (2020). Smart Monitoring of the Status of Driver Using the Dashboard Vehicle Camera. International Journal of New Practices in Management and Engineering, 9(01), 01 - 07. https://doi.org/10.17762/ijnpme.v9i01.81

Saad, L. B., Chauvenet, C., & Tourancheau, B. (2011, September). Simulation of the RPL Routing Protocol for IPv6 Sensor Networks: two cases studies. In International Conference on Sensor Technologies and Applications SENSORCOMM 2011. IARIA.

Airehrour, D., Gutierrez, J. A., & Ray, S. K. (2019). SecTrust-RPL: A secure trust-aware RPL routing protocol for Mobile Ad-hoc Network. Future Generation Computer Systems, 93, 860-876.

Tripathi, J., de Oliveira, J. C., & Vasseur, J. P. (2010, March). A performance evaluation study of rpl: Routing protocol for low power and lossy networks. In 2010 44th Annual Conference on Information Sciences and Systems (CISS) (pp. 1-6). IEEE.

Zhao, M., Ho, I. W. H., & Chong, P. H. J. (2016). An energy-efficient region-based RPL routing protocol for low-power and lossy networks. IEEE Mobile Ad-hoc Network Journal, 3(6), 1319-1333.

Gaddour, O., Koubâa, A., Baccour, N., & Abid, M. (2014, May). OF-FL: QoS-aware fuzzy logic objective function for the RPL routing protocol. In 2014 12th International symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt) (pp. 365-372). IEEE.

Kim, H. S., Kim, H., Paek, J., & Bahk, S. (2016). Load balancing under heavy traffic in RPL routing protocol for low power and lossy networks. IEEE Transactions on Mobile Computing, 16(4), 964-979.

Veeraiah, N., Khalaf, O. I., Prasad, C. V. P. R., Alotaibi, Y., Alsufyani, A., Alghamdi, S. A., & Alsufyani, N. (2021). Trust aware secure energy efficient hybrid protocol for manet. IEEE Access, 9, 120996-121005.

Ahmad, S. J., Unissa, I., Ali, M. S., & Kumar, A. (2022). Enhanced security to MANETs using digital codes. Journal of Information Security and Applications, 66, 103147.

Jim, L. E., Islam, N., & Gregory, M. A. (2022). Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes. Computers & Security, 113, 102538.

Khan, B. U. I., Anwar, F., Olanrewaju, R. F., Kiah, M. L. B. M., & Mir, R. N. (2021). Game Theory Analysis and Modeling of Sophisticated Multi-Collusion Attack in MANETs. IEEE Access, 9, 61778-61792.

Sánchez, F., ?or?evi?, S., Georgiev, I., Jacobs, M., & Rosenberg, D. Exploring Generative Adversarial Networks for Image Generation. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/147

Ahmed, Ahmed & Rashid, Sami & Abbas, Ali & Abdulsattar, Nejood Faisal & Hassnen, Th & Mansour, Shakir & Mustafa, Th & Hassan, Mustafa & Habelalmateen, Mohammed. (2022). An Effectual Secure Cryptography Scheme for Multipath Routing in A Wsn-Based IoT Environment. 10.1109/IICETA54559.2022.9888604.

Veeraiah, N., Khalaf, O. I., Prasad, C. V. P. R., Alotaibi, Y., Alsufyani, A., Alghamdi, S. A., & Alsufyani, N. (2021). Trust aware secure energy efficient hybrid protocol for manet. IEEE Access, 9, 120996-121005.

Srilakshmi, U., Veeraiah, N., Alotaibi, Y., Alghamdi, S. A., Khalaf, O. I., & Subbayamma, B. V. (2021). An Improved Hybrid Secure Multipath Routing Protocol for MANET. IEEE Access, 9, 163043-163053.

Simpson, S. V., & Nagarajan, G. (2021). A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment. Future Generation Computer Systems, 125, 544-563.

Tu, J., Tian, D., & Wang, Y. (2021). An active-routing authentication scheme in MANET. IEEE Access, 9, 34276-34286.

Sowjanya, K., Dasgupta, M., & Ray, S. (2021). A lightweight key management scheme for key-escrow-free ECC-based CP-ABE for IoT healthcare systems. Journal of Systems Architecture, 117, 102108.

Bondada, P.; Samanta, D.; Kaur, M.; Lee, H.-N. Data SecurityBased Routing in MANETs Using Key Management Mechanism. Appl. Sci. 2022, 12, 1041