# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 3 Methodology
### VAEs for WSNs  
In the proposed work, the VAEs are utilized for compressing WSN data, using a neural network architecture with the help of an encoder and decoder. The encoder is placed on the source to compress the generated data from the sensor nodes as a lower dimensional latent space, while the decoder is placed on the destination to reconstruct the received data into its original value. Therefore, the property of the encoder in VAE is modified in the proposed work with a fully connected layer, for making the model suitable for reading the numerical inputs from the dataset with reduced power consumption, and the ReLU activation function is included for improving the learning stability with the limited computational resources. It is achieved by preventing the overfitting issues on the learning process. Similarly, a well distributed latent space is required for reducing the reconstruction errors of VAE, and that is achieved by fine tuning the model parameters. The latent dimensionality and weights on the reconstruction and \(KL\) divergence loss are customized in this work, to make VAE suitable for the proposed application. The performance of the trained VAE can be improved by having a continuous optimization or fine-tuning process while implemented on the real time WSN architectures. This iterative approach improves the performance of VAE, based on the feedback received from the actual network. The energy utilization of the VAE can also be improved by this iterative approach, as it operates the essential data with minimal resources. The performance of the proposed model is also improved on diverse and variational data patterns, as the network is retrained regularly by varying the hyperparameters of VAE. The integration of VAE to WSN provides a better lifespan on WSNs, as it reduces the amount of data transmission through compression technique. The compression remains effective as VAE preserves the crucial information on the generatedsensor data. Similarly, VAE improves the data processing ability of the WSNs that results in effective decision making and analysis on the data transmission process. Overall, the proposed work is found to be suitable for WSN applications that are implemented with limited resources. The algorithmic flow of VAE in WSN is descriptimeasurements. The performance of the CS can be improved further when it is implemented with convex optimization. Lightweight temporal compression is a time series sensor data compression technique used for reducing the transmission overhead. LTC stores only the changes on the transmitting data rather than storing them all together. This minimizes the amount of data transmitted between the source and destination, by preserving the major temporal trends and patterns of the sensor data. The encoding of LTC is simple, and that does not require any major computational facility. As the encoding is simple, the energy utilization of LTC is also minimal in nature. This makes the LTC well suited for several WSN applications. Autoencoder is an unsupervised learning approach for efficient data representation. AEs are best in reducing the reconstruction error, and that improves the compression efficiency. This makes AE suitable for complex WSNs. To evaluate the performance of the proposed work, the VAEs are finetuned for the WSN application with customized encoder and decoder layers. As the dataset used for the work contains the information of 54 sensors, the input layer is structured with 54 different neurons for gathering the data in an efficient way. The hidden layers included between the input and the latent space layer were reduced gradually for observing the useful information from the given data. The neuron layers at the latent space are customized dynamically with respect to the feedback received from the output layer. Table 1 represents the number of neurons in each layer. MATLAB R2023a simulation is utilized in the work for evaluating the performance of the proposed concept with the existing one.