Design and Implementation of Fuzzy Logic Based OLSR to Enhance the Performance in Mobile Ad Hoc Networks

Vikkurty, Sireesha; Shetty, S. Pallam

doi:10.1007/978-981-13-8461-5_47

Cited by 3 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this manuscript, the proposed ANN based OLSR protocol's effectiveness is validated by means of EC, ROR, PDR, and EED. In addition, the ANN based OLSR protocol performance is analysed by comparing its results with the existing protocols like AODV, Dynamic Source Routing (DSR), Zone Routing Protocol (ZRP), Destination Sequenced Distance Vector (DSDV), OLSR, Fuzzy Logic based OLSR [13], OLSR with multi-objective ALO algorithm [14], OLSR with bat algorithm [16], MAODV-ACO [19], and PSNHF based AODV protocol [20]. The performance is analysed by varying the node size such as 40, 50, 60, 70, 80, 90, and 100.…”

Section: Resultsmentioning

confidence: 99%

“…Vikkurty and Pallam Shetty [13] evaluated the effectiveness of the fuzzy logic based OLSR protocol in MANET for different network sizes. The extensive experiment confirmed that the presented protocol performed effectively in the large, medium, and small networks related to the conventional protocols.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, the proposed ANN based OLSR protocol's performance is validated in terms of EED. The obtained results are compared with conventional protocols like AODV, DSR, DSDV, OLSR, ZRP, fuzzy logic based OLSR [13], OLSR with multiobjective ALO [14] and OLSR with bat algorithm [16] by varying the node size like 40, 50, 60, 70, 80, 90, and 100. By inspecting Table 2, the proposed ANN based OLSR protocol almost reduced 3 to 4 times of EED compared to the existing routing protocols in MANET.…”

Section: Quantitative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

2022

IJIES

View full text Add to dashboard Cite

Mobile Ad hoc Network (MANET) includes many wireless mobile nodes, and higher energy consumption is the major concern in MANET. Compared to other protocols, the Optimized Link State Routing Protocol (OLSR) is a superior pro-active protocol, which works on limited configuration attribute variables in the MANET. In a dynamic environment, default variables of the attribute are not suitable as per the IETF draft. Therefore, the Artificial Neural Network (ANN) is integrated with the OLSR protocol in this manuscript. In addition, the OPNET modeler is used to evaluate the performance of ANN based OLSR protocol, which is simulated by utilizing the MATLAB software environment. The performance measures such as Routing Overhead Ratio (ROR), End to End Delay (EED), Energy Consumption (EC), and Packet Delivery Ratio (PDR) are employed for analysing the protocol's performance. The simulation result revealed that the ANN based OLSR protocol effectively increases the network lifespan and reduces EC of the MANET network. Compared to the Ant Colony Optimization (ACO) based Modified Ad-hoc On Demand Distance Vector (MAODV) and Prevention of Selfish Node using Hash Function (PSNHF) based AODV protocols, the proposed ANN based OLSR protocol achieved higher PDR of 99.92%, 99.90%, and 99.89% on the nodes of 80, 90 and 100.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Quantitative Analysismentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…In order to implement the same, the most important factor to be found for the conduction of experiments and in the current model, the TTL had chosen as the most significant and important factor among them. The performance of FB-TTL-STAR with different TTL fuzzy values for different network sizes (26,52,78) and mobility speeds (2,4,6) m/sec is evaluated. At last, the performance of the current model is being compared with the other models and the results are tabulated and present in the results section.…”

Section: Time To Live =mentioning

confidence: 99%

“…Examining completely different routing protocols performance in dynamic surroundings plays a significant role in economical routing. Mobile AdHoc networks are the wireless networks that were largely employed in the recent times within the real time world applications [2,3,4]. The nodes within the mobile networks are changing from time to time with relevance their position of the nodes within the actual design of the network.…”

Section: Introductionmentioning

confidence: 99%

Improved Real Time Communication Using Star Routing Protocol In Manets Influenced By Fuzzy Logic

Rao¹,

Shetty²

2019

IJCA

View full text Add to dashboard Cite

In Mobile Ad hoc Networks, routing is a challenging issue. Basically, the working routing protocols for these networks are classified in to three categories like the reactive protocols, proactive protocols and the hybrid t protocols which were the combination of above two types of protocols. Among them proactive routing protocols category is selected for the present study. In proactive routing protocols, STAR with LORA approach is chosen because it has less control overhead when compared to ORA approach and other proactive routing protocols. It works on few defacto parameter values in a dynamic MANET environment. These static values for parameters are not suitable in a dynamic environment. With reference to the IETF draft, it is a time series problem. In this paper, an effort has been made to incorporate the soft computing technique, fuzzy logic based STAR to enhance the performance of a MANET to support real time communication. The proposed Fuzzy logic approach based STAR performance is evaluated using simulation through QualNet simulator. From the results, it is observed that the Fuzzy logic based STAR provides superior performance than the defactoSTAR protocol. The comparative performance was measured using the performance metrics End-to-end delay, Jitter and throughput. A number of simulation scenarios were executed for small, medium and large size networks. From the simulation results, we conclude that Fuzzy Logic based STAR outperforms for small, medium size networks.

show abstract