Grid based Bio-Inspired Energy Efficient Trusted Hybrid Routing Protocol for MANET
Nareshkumar R. M.1, S. Phanikumar2
1Nareshkumar R. M., Research Scholar, Department of Computer Engineering, GITAM University, Hyderabad-502329, India.
2S. Phanikumar, Professor & HOD, Computer Engineering, GITAM University, Hyderabad-502329, India.
Manuscript received on 06 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 6856-6864 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5831098319/2019©BEIESP | DOI: 10.35940/ijrte.C5831.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Device to device (D2D) data communication in cellular wireless network can be done either directly from source equipment (SE) to destination equipment (DE) or through relay equipment (RE). This type of network mainly known as Wireless ad hoc networks. Mobile ad-hoc networks (MANETs) have constraints such as such as limited power, route failure, bandwidth allocation, and computational complexity. The major impact on the performance of network depends on routing strategy. Routing strategy for matching the multi constraints of network is so far challenge in conventional network. MANETs suffers from dynamic movement of node, power, dynamic routing and storage complexity. Dynamic movement force the computation of re-routing, which act as extra computational burden on the system. In this paper, we propose the Grid Cluster based Multi-Objective Genetic Algorithm for Energy Efficient Trusted Network (gCMOGAEETN) algorithm for finding optimal routes from a given SE to a given DE. Our simulation results show that gCMOGAEETN algorithms are efficient in solving these routing problems and are capable of finding the optimal solutions at lower complexity than the ’brute-force’ exhaustive search, when the number of user equipment (UEs) is higher than or equal to 50. The analytical and simulation result shows that proposed method exhibit significantly higher performance than optimal adaptive forwarding strategy (OAFS), sub-optimal adaptive forwarding strategy (SAFS), memory enhanced genetic algorithm (MEGA), Elitism-based Immigrants Genetic algorithm (EIGA), dynamic load-balanced clustering problem (DLBCP) and Genetic Algorithm Based Optimization of Clustering (GABOC).
Keywords: Bio-Inspired Algorithm, Genetic algorithm, Mobile Ad hoc Network, Trust factor.
Scope of the Article: Mobile Ad hoc Network