Wireless Communication Networks Projects with Source Code MATLAB

Project Discription

As the genetic algorithm was utilize in different type of analysis so for the same all need to increase the channel utilization in the required area. So contributing the wireless network is done in this work by the proposed method for sharing the channel between the primary and secondary users. In the proposed work Frog Jumping Sorensen Wireless Network Optimization (FJSWNO) channel was optimize by selecting good set of nodes with malicious node detection algorithm. Each node in the network goes under some observation window, to calculate the trust value of the node as per performance in observation window.

Conclusion

Work has resolved the issue of dynamic link development for the secondary units. by end to end path for data transfer where spectrum utilization, interference and lesser intermediate node were consider as main objective. To achieve those objective genetic algorithm frog jumping Optimization was used. Genetic algorithm generate dynamic path as per node positions and spectrum channel. This work apply frog memplex phase for finding end to end path. Further paper evaluate Sorensen trust value help work to identify malicious node and stop packet routing in malicious path, this increase channel utilization of the work. Experiment was done on different number of secondary nodes for various possible links. Results are compare with previous existing approach and it was found that FJSWNO has low transfer time values as compare to the previous approaches work.

As the genetic algorithm was utilize in different type of analysis so for the same all need to increase the channel utilization in the required area. So contributing the wireless network is done in this work by the proposed method for sharing the channel between the primary and secondary users. In the proposed work Frog Jumping Sorensen Wireless Network Optimization (FJSWNO) channel was optimize by selecting good set of nodes with malicious node detection algorithm. Each node in the network goes under some observation window, to calculate the trust value of the node as per performance in observation window.

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