Topology control for minimizing interference with delay constraints in an ad hoc network

Wu, Yu-Fei; Hu, Yachao; Su, Yi-Wen; Yu, Ning; Ran, Feng

doi:10.1016/j.jpdc.2017.10.005

Cited by 9 publications

(23 citation statements)

References 18 publications

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“…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. (20) From Equation 20, luciferin value ' i N l ' for each mobile node in network is computed. In NIGSMO algorithm, the glowworm with less luciferin value is attracted towards to higher one.…”

Section: Route Path Identificationmentioning

confidence: 99%

“…Step 11: Evaluate' i N l ' using (19) Step 12:Measuremoving probability 'Pr' using (20) Step 13:Update location '…”

Section: Route Path Identificationmentioning

confidence: 99%

“…Step 11: Evaluate' i N l ' using (19) Step 12:Measuremoving probability 'Pr' using (20) Step 13:Update location ' 1) (t w 1 N + ' using (21) Step 14: Update luciferin value l(t+1) (22) Step 15:If ' Step 24:NNi reply to SN with CTS message before the timer expires…”

Section: //Statistical Markov Model Based Natural Inspired Glowworm Mmentioning

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%

“…A Localized Delay-constrained Bellman-Ford (LDBF) algorithm was introduced in [20] to minimize interference while considering the delay constraint. However, collision reduction during data routing remained an open issue.…”

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: Route Path Identificationmentioning

confidence: 99%

“…Step 11: Evaluate' i N l ' using (19) Step 12:Measuremoving probability 'Pr' using (20) Step 13:Update location '…”

Section: Route Path Identificationmentioning

confidence: 99%