Data gathering based on hybrid energy efficient clustering algorithm and DCRNN model in wireless sensor network

In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network (WSN) caused by uneven energy consumption among nodes, a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm (FF-PIA) is proposed to optimise the data transmission path. After having obtained the optimal number of cluster head node (CH), its result might be taken as the basis of producing the initial population of FF-PIA algorithm. The Levy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search. Moreover, a Gaussian perturbation strategy is applied to update the optimal solution, ensuring the algorithm can jump out of the local optimal solution. And, in the WSN data gathering, a one-dimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks (DCRNN). We conducted experiments on the National Oceanic and Atmospheric Administration (NOAA) dataset. It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance. FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.