SDWSN Routing Optimization Algorithm Based on Adaptive Factors and Gaussian Perturbation Particle Swarm Algorithm

For the traditional routing algorithms in software-defined wireless sensor networks in the application of the problem of uneven energy consumption and low energy utilization, an energy balanced routing algorithm GDPSO based on adaptive learning factor and Gaussian perturbation particle swarm optimization is proposed. Software-defined wireless sensor networks will be the data flow logic centralized to the control plane to obtain global information, the algorithm in the PSO on the basis of the increased Gaussian perturbation and dynamically adjust the adaptive silver improved algorithm, comprehensively consider the node residual energy, global particle location information, neighboring nodes and other factors to construct the optimal fitness function to select the optimal cluster head, reasonable division of the global sub-cluster, in the inter-cluster communication, combined with the link energy consumption, the global particle energy residual, to build the shortest routing tree. The results show that the improved algorithm can be adjusted dynamically according to the network cycle routing strategy, equalize energy consumption and extend network implementation time.