Factors affecting the performance of ad hoc on-demand distance vector protocol in simulated mobile ad hoc network scenarios using Taguchi approach
The performance of ad hoc on demand vector (AODV) protocol is affected hugely by some common major factors. These factors are terrain, network size, node velocity, pause time, transmission range, traffic load, and packet rates. The main purpose of this study is to analyse the effects of those factor...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/78046/1/TanJunBinMFS20141.pdf |
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| Summary: | The performance of ad hoc on demand vector (AODV) protocol is affected hugely by some common major factors. These factors are terrain, network size, node velocity, pause time, transmission range, traffic load, and packet rates. The main purpose of this study is to analyse the effects of those factors and some selected two-way interactions on the performance measure of drop rates and average end-to-end delay. Taguchi approach was used in this study. Initially, L16 orthogonal array was used to determine the effects of the seven main factors and eight others two-way interactions between selected factors. The final results revealed that terrain, network size, transmission range, and traffic load have significant effects on drop rates. On the other hand, we discovered that terrain, transmission range, traffic load and interaction between node velocity and pause time have significant effects on average end-to-end. Interaction plot for L16 singled out strong interaction between node velocity and pause time for the effect on average end-to-end delay. Furthermore, L8 orthogonal array was applied to analyse the seven main factors only since most of the interactions effects from L16 were largely insignificant to the response. The most influential factors affecting the drop rates (in descending order) were terrain, transmission range, pause time, network size, packet rates, node velocity, and traffic load. For average end-to-end delay, the most influential factors (in descending order) were transmission range, pause time, terrain, network size, traffic load, packet rates, and node velocity. ANOVA results for L8 shows that terrain and transmission range have significant effects on drop rates. For average end-to-end delay, terrain, pause time and transmission range have significant effects on the response. |
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