|
|
 |
| |
|
|
|
Statistical Performance Analysis of Wireless Network Enhancement Using Fuzzy Domination Graphs |
|
|
|
PP: 781-794 |
|
|
doi:10.18576/jsap/140619
|
|
|
|
Author(s) |
|
|
|
Suleiman Ibrahim Mohammad,
Yogeesh N,
Mohammed Almakki,
Asokan Vasudevan,
Ashalatha K. S,
Mohammad Hunitie,
|
|
|
|
Abstract |
|
|
| In this paper we propose an experimental study to use fuzzy domination graphs with respect to applications and
parameters of wireless network optimization. This paper introduces an experimental procedure to gather data from wireless communication network in real environment, then proposes a mathematical model using the fuzzy domination graphs to design the coverage and connectivity in the network. Experiments were conducted measuring the network coverage, connectivity, and throughput using performance metrics to estimate how effective the proposed fuzzy domination graph approach is over other optimization methods. Our results reveal that the fuzzy domination graph approach is able to yield high network coverage and connectivity in comparison to the other methods. We close with consideration of the implications and imposed of our results, and with recommendations for additional work. In summary, demonstrated with detailed case analysis of wireless network optimization using fuzzy domination graphs, the experimental results show that Fuzzy- Dominating-Graphs perform better to maintain trade-offs among coverage, throughput and energy consumption. The model utilizes network connections as a fuzzy adjacency matrix, where the presence of links between nodes is defined by membership values that indicate link strength rather than absolute 0 or negative/positive weights and identifies dominant nodes that maximize performance. The optimization allocates resources efficiently by dynamically adjusting transmission power for influential nodes while minimizing energy consumption of peripheral ones. This approach is superior to conventional methods, especially when dealing with uncertain or changing network environments. The research, which has potential applications in smart cities, IoT and the future 6G networks shows that these so-called fuzzy domination graphs are
a good strategy to improve efficiency of wireless network. |
|
|
|
|
|
|
|
