IRDFPR-CMDNN: An energy efficient and reliable routing protocol for improved data transmission in MANET

Mobile Ad hoc Networks (MANET) are structure less, autonomous wireless networks with mobile nodes that dynamically establish data transmission connections. Due to dynamic topological change, MANET routes are unbalanced and break repeatedly. Hence, providing efficient and reliable data delivery with effective utilization of network resources is a challenging issue to be considered in MANET. This paper proposes an instant-runoff Ranked Decision Forests Probit Regression-based Connectionist Multilayer Deep Neural Network (IRDFPR-CMDNN) for efficient data transmission and higher data delivery with a minimum end-to-end delay. This IRDFPR-CMDNN method performs route identification, data delivery, and route maintenance with more than three layers. Then the mobile nodes are sent to the input layer of the Connectionist Multilayer Deep Neural Network. In hidden layer 1, the Instant-runoff Ranked Decision Forests algorithm is applied for classifying the mobile nodes depending on the residual energy and load capacity. With selected mobile nodes, the Probit Regression is applied for finding the nearest neighboring nodes in the second hidden layer based on the link quality and received signal strength for route path establishment. Then multiple paths for routing are established from source to destination node and start to perform the data transmission. If link failure occurs during the data transmission, another alternative route with better link quality is selected for routing. In this way, energy-efficient data transmission is performed from source to destination with a higher data delivery rate and minimal time consumption. Experimental evaluation is carried out on energy consumption, packet delivery ratio, packet drop rate, throughput, and end-to-end delay with varying numbers of mobile nodes and data packets. Simulation results show that the IRDFPRCMDNN technique effectively enhances data delivery, throughput and minimizes energy consumption, packet loss rate, delay with respect to conventional methods.

1. Banerjee I., Warnier M., Brazier F.M.T. Self-organizing topology for energy-efficient ad-hoc communication networks of mobile devices // Complex Adaptive Systems Modeling. 2020. V. 8. N 1. P. 7. https://doi.org/10.1186/s40294-020-00073-7

2. Zhang T., Zhao S., Cheng B. Multipath routing and MPTCP-based data delivery over MANETS // IEEE Access. 2020. V. 8. P. 32652– 32673. https://doi.org/10.1109/ACCESS.2020.2974191

3. Chen Z., Zhou W., Wu S., Cheng L. An adaptive on-demand multipath routing protocol with QoS support for high-speed MANET // IEEE Access. 2020. V. 8. P. 44760–44773. https://doi.org/10.1109/ACCESS.2020.2978582

4. Shailaja P., Rao C.V.G. Zone assisted mobility aware multipath routing (ZM2R) for energy constrained MANETs // Materials Today: Proceedings. 2021. V. 37. Part 2. P. 3434–3441. https://doi.org/10.1016/j.matpr.2020.09.287

5. Prasad P.R., Shankar S. Efficient performance analysis of energy aware on demand routing protocol in Mobile Ad-Hoc Network // Engineering Reports. 2020. V. 2. N 3. P. e12116. https://doi. org/10.1002/eng2.12116

6. Almazok S.A., Bilgehan B. A novel dynamic source routing (DSR) protocol based on minimum execution time scheduling and moth flame optimization (MET-MFO) // EURASIP Journal on Wireless Communications and Networking. 2020. V. 2020. N 1. P. 219. https://doi.org/10.1186/s13638-020-01802-5

7. Shivakumar K.S., Patil V.C. An optimal energy efficient cross-layer routing in MANETs // Sustainable Computing: Informatics and Systems. 2020. V. 28. P. 100458. https://doi.org/10.1016/j.suscom.2020.100458

8. Deepa J., Sutha J. A new energy based power aware routing method for MANETs // Cluster Computing. 2019. V. 22. P. 13317–13324. https://doi.org/10.1007/s10586-018-1868-x

9. Almolaa O.S., Kashmola M.Y. Distributed deep reinforcement learning computations for routing in a software-defined Mobile Ad Hoc Network // Turkish Journal of Computer and Mathematics Education. 2021. V. 12. N 6. P. 1708–1721. https://doi.org/10.17762/turcomat.v12i6.3378

10. Prakasi O.S.G., Varalakshmi P. Decision Tree Based Routing Protocol (DTRP) for reliable path in MANET // Wireless Personal Communications. 2019. V. 109. N 1. P. 257–270. https://doi.org/10.1007/s11277-019-06563-z

11. Alkadhmi M.M.A., Uçan O.N., Ilyas M. An efficient and reliable routing method for hybrid Mobile Ad Hoc Networks using deep reinforcement learning // Applied Bionics and Biomechanics. 2020. V. 2020. P. 8888904. https://doi.org/10.1155/2020/8888904

12. Tilwari V., Dimyati K., Hindia M.H.D.N., Fattouh A., Amiri I.S. Mobility, residual energy, and link quality aware multipath routing in MANETs with Q-learning algorithm // Applied Science. 2019. V. 9. N 8. P. 1582. https://doi.org/10.3390/app9081582

13. Durr-e-Nayab, Zafar M.H., Altalbe A. Prediction of scenarios for routing in MANETs based on expanding ring search and random early detection parameters using machine learning techniques // IEEE Access. 2021. V. 9. P. 47033–47047. https://doi.org/10.1109/ACCESS.2021.3067816

14. Vaighan M.G., Jamali M.A.J. A multipath QoS multicast routing protocol based on link stability and route reliability in mobile ad-hoc networks // Journal of Ambient Intelligence and Humanized Computing. 2019. V. 10. N 1. P. 107–123. https://doi.org/10.1007/s12652-017-0609-y

15. Priyambodo T.K., Wijayanto D., Gitakarma M.S. Performance optimization of MANET networks through routing protocol analysis // Computers. 2020. V. 10. N 1. P. 1–13. https://doi.org/10.3390/computers10010002

16. Nallusamy C., Sabari A. Particle swarm based resource optimized geographic routing for improved network lifetime in MANET // Mobile Networks and Applications. 2019. V. 24. N 2. P. 375–385. https://doi.org/10.1007/s11036-017-0911-0

17. Abdalim T.-A.N., Hassan R., Muniyandi R.C., Aman A.H.M., Nguyen Q.N., Al-Khaleefa A.S. Optimized particle swarm optimization algorithm for the realization of an enhanced energyaware location-aided routing protocol in MANET // Information. 2020. V. 11. N 11. P. 1–17. https://doi.org/10.3390/info11110529

18. Anbarasan M., Prakash S., Anand M., Antonidoss A. Improving performance in mobile ad hoc networks by reliable path selection routing using RPS-LEACH // Concurrency and Computation: Practice and Experience. 2019. V. 31. N 7. P. e4984. https://doi.org/10.1002/cpe.4984

19. Yang B., Wu Z., Shen Y., Jiang X. Packet delivery ratio and energy consumption in multicast delay tolerant MANETs with power control // Computer Networks. 2019. V. 161. P. 150–161. https://doi.org/10.1016/j.comnet.2019.06.003

20. Farheen N.S.S., Jain A. Improved routing in MANET with optimized multi path routing fine tuned with hybrid modeling // Journal of King Saud University — Computer and Information Sciences. 2020. in press. https://doi.org/10.1016/j.jksuci.2020.01.001

21. Li Z., Uusitalo M.A., Shariatmadari H., Singh B. 5G URLLC: Design challenges and system concepts // Proc. of the 15th International Simposium on Wireless Communication Systems (ISWCS). 2018. P. 8491078. https://doi.org/10.1109/ISWCS.2018.8491078

22. Jayaramu A.B., Banga M.K. Delay aware routing protocol using optimized AODV with BBO for MPLS-MANET // International Journal of Intelligent Engineering and Systems. 2020. V. 13. N 5. P. 29–37. https://doi.org/10.22266/ijies2020.1031.04