An Improved Data Compression Framework for Wireless Sensor Networks Using Stacked Convolutional Autoencoder (S-CAE) - Kumble, Lithin; Patil, Kiran Kumari (SN computer science, 2023)
Data compression is crucial in the networks as there is limited energy which is accessible to sensor nodes in wireless sensor networks (WSNs). The sensor nodes lifetime is extended enormously by reducing the data reception and transmission. We introduce a stacked convolutional RBM auto-encoder (stacked CAE) model for compressing sensor data, which is made up of layers: an encode layer and a decode layer, both of which are discussed. The encode layer is used to compress and decompress data from sensor and then, the decode layer is used for reconstruction and compression of the data from sensors. Both encode and decode layers are comprised of four standard restricted Boltzmann machines, which are employed throughout the system. This work focuses on energy reduction technique by reduction of model’s parameters which in turn reduces the model’s calculation and storage energy. The model's effectiveness is evaluated against the Intel Lab data. The average temperature reconstruction inaccuracy is 0.312 °C, the average percentage RMS difference is 9.84%, and the figures imply that the model's compression ratio is 10. Hence, there is a possibility of minimizing the consumption of energy of node communication in WSNs by 92%. The new model attains higher compression proficiency and remark precision while keeping the same pressure ratio when in comparison with the previous method.