AODV with intelligent priority flow scheme for multi-hop ad hoc networks

Lee, Zne-Jung; Chou, So-Tsung; Lee, Chou‐Yuan; Peng, Bin-Yu

doi:10.1007/s40595-016-0072-2

Cited by 2 publications

(17 citation statements)

References 12 publications

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“…This helps for SMM-NIGMO technique to increases the ratio of a number of data packets that are successfully delivered to destination mobile node in MANET as compared to other conventional works. As a result, SMM-NIGMO technique enhances the throughput by 17 % and 11 % as compared to state-of-the-art works LDBF algorithm [20] and ABCA [23] respectively.…”

Section: Performance Measure Of Throughputmentioning

confidence: 99%

“…With the minimization of interferences, SMM-NIGMO technique also minimizes number of collisions and retransmissions and consequently lowers the amount of energy consumed for efficient data delivery in MANET. (23) From Equation 23, 'l (t+1) ' refers to the updated luciferin value, 'C' is a luciferin decay constant '…”

Section: Route Path Identificationmentioning

confidence: 99%

“…The simulation parameter used for performing experimental work is shown below. The simulation result of SMM-NIGMO technique is compared against two existing works, namely, Localized Delay-constrained Bellman-Ford (LDBF) algorithm [20] and Artificial Bee Colony Algorithm (ABCA) [23]. The simulation processes of SMM-NIG-MO Technique is conducted for many factors such as throughput, energy consumption, data loss rate and an End-to-End delay with respect to a diverse number of mobile nodes and data packets.…”

Section: Simulation Settingsmentioning

confidence: 99%

“…In order to evaluate the throughput, SMM-NIGMO Technique is implemented in NS-2 simulator by considering different number of data packets in the range of 25-250. When taking the 100 data packets to conduct the simulation work, SMM-NIGMO Technique gets 91 pps throughput whereas state-of-the-art works LDBF algorithm [20] and ABCA [23] obtains 82 pps and 84 pps respectively. From the above-obtained results, it is expressive that the throughput using SMM-NIGMO Technique is enhanced than other existing works.…”

Section: Performance Measure Of Throughputmentioning

confidence: 99%

“…However, collision reduction during data routing remained an open issue. Artificial bee colony algorithm (ABCA) was designed in [23] to reduce the data collision and to increase data transmission performance in the adhoc network. However, the amount of energy utilized for data delivery was more.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Markov Model Based Natural Inspired Glowworm Swarm Multi-Objective Optimization for Energy Efficient Data Delivery in Manet

Perumal¹

2020

ITC

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An interference and collision is a significant problem to be solved in Mobile Ad Hoc Network (MANET). Few techniques were developed in existing work to perform energy aware routing in MANET. But, handling both interference and collision on route path was remained open issues which increases the energy usage and reduces the throughput of MANET. In order to overcome such limitations, Statistical Markov Model Based Natural Inspired Glowworm Multi-objective Optimization (SMM-NIGMO) technique is proposed. The SMM-NIGMO technique contains three key processes namely interference prediction, optimal node selection, route path identification. The SMM-NIGMO technique at first proposes a Statistical Markov Model to determine the interference level of each mobile node in MANET at the time ‘ ’. After that, SMM-NIGMO Technique designs a Natural Inspired Glowworm Swarm Multi-Objective Optimization (NIGSMO) algorithm to carry out the optimal node selection process in MANET. During this process, SMM-NIGMO Technique finds the mobile nodes with lower inferences and higher residual energy as optimal in dynamic environment. After discovering the best nodes, SMM-NIGMO Technique uses the Request-To-Send (RTS) and Clear-To-Send (CTS) mechanism for identifying the best route path in MANET. During the route path finding process, SMM-NIGMO Technique selects the mobile node to transmit the packets to destination without any collisions. As a result, SMM-NIGMO Technique enhances the performance of energy efficient data delivery in MANET as compared to existing works. The simulation of SMM-NIGMO Technique is performed using parameters such as throughput, energy consumption, packet loss rate and end to end delay. The simulation result depicts that the SMM-NIGMO Technique is able to improve the throughput and also reduces the energy consumption of data transmission in MANET as compared to state-of-the-art works.

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Section: Performance Measure Of Throughputmentioning

confidence: 99%

Section: Route Path Identificationmentioning

confidence: 99%

Section: Simulation Settingsmentioning

confidence: 99%

Section: Performance Measure Of Throughputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Statistical Markov Model Based Natural Inspired Glowworm Swarm Multi-Objective Optimization for Energy Efficient Data Delivery in Manet

Perumal¹

2020

ITC

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MD-MARS: Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

et al. 2023

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The demand for Wireless Sensor Networks is increasing day by day because of their diverse nature. Due to the limited energy, it is a complex task to retract the sensor node after deployment. So, there is a requirement for network maintainability before the deployment phase for its smooth working. It is achieved in three phases: hardware of the sensor node, communication and external environmental phase. This paper focuses on network maintainability in the communication phase. A novel framework MD-MARS is presented to enhance the network maintainability. This framework is classified into three phases namely analysis of performance parameters, data flow optimization and maintainability evaluation. In the initial phase, the performance parameter is analyzed using NS2 simulator. The next phase deals with data flow optimization using a machine learning algorithm. It reduces congestion and enhances network performance. The proposed algorithm is finely tuned to different degrees using the Grid Search approach to achieve the highest accuracy. The best model is selected based on accuracy and minimizes the prediction error. This algorithm predicts with the highest accuracy of 99.83%, lowest being 21.17%. Maintainability is achieved in the last phase using the total time taken to optimize the data flow. Several observations of repair time are determined for the best-tune model during the prediction of optimized data flow. These observations are used to calculate the mean time to repair, standard deviation, probability density function, maintainability and repair rate. The maximum maintainability achieved in this paper is 97.67% at a repair time of 26.07 milliseconds.INDEX TERMS Data flow prediction, maintainability, multivariate adaptive regression splines (MARS), Quality of Service, repair time, wireless sensor network.

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AODV with intelligent priority flow scheme for multi-hop ad hoc networks

Cited by 2 publications

References 12 publications

Statistical Markov Model Based Natural Inspired Glowworm Swarm Multi-Objective Optimization for Energy Efficient Data Delivery in Manet

Statistical Markov Model Based Natural Inspired Glowworm Swarm Multi-Objective Optimization for Energy Efficient Data Delivery in Manet

MD-MARS: Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

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