## 3 Data Prediction for Energy Saving in WSNs
### Related Works to Data Reduction for Energy Saving in WSNs
The scheme uses a synchronous prediction model to reduce the transmission from sensors to the Sink using an auto-regressive analysis algorithm. The scheme is evaluated using real-world data through simulations. The results show that the method reduces the traffic in the WSNs and, consequently, the energy consumption of sensor nodes. A prediction model based on Bidirectional LSTMs, in what the authors called the multi-node multi-feature model (MNMF), was proposed in [44]. The algorithm exploits the correlation between sensors in the same location to improve the quality of the predictions, merging the data from different sensor nodes in a multi-headed Neural Network. The architecture was compared with an Elman Neural Network, which is a typical local forward network, Nonlinear Autoregressive Exogenous Model (NARX), and GRNN, presenting much better results in terms of prediction accuracy and concluding that a spatial-temporal correlation can improve the model performance; however, the reduction in transmissions was not included in the work. This is a complex algorithm that requires the combination of data from multiple sensors into a single model and the transmission of multiple sensors to a CH or BS in order to compute the model. The authors in [45] also consider the spatial-temporal correlation by combining data from three sensor nodes in a final FCL. The model uses a combination of 1D CNN to extract high level time-independent features and Bidirectional LSTMs for temporal correlation and time-dependent features, such as trends and seasonality, in an End-to-End DL approach. The performance was compared with a CNN, a Bidirectional LSTM, and a GRU, presenting a smaller error in prediction. As the algorithm needs to exploit the correlation between sensors, it must be trained in the Base Station or Cluster Heads, and the correlation between the sensors must be known a priori. The authors concluded that the model can make a stable and accurate prediction on temperature and humidity data in the short and medium term. In [46], the authors proposed a model to reduce redundant transmission in a WSN, named Temporal Data Prediction-based Aggregation (TDPA), and an energy prediction-based cluster head rotation algorithm for load balancing within clusters. The paper presents a structure-independent algorithm that can be coupled with any clustered network. The algorithm was tested with a real-world dataset of hourly temperature readings, showing better results in energy saving than the similar algorithms that were used for comparison (SAF, PAQ, and CoGKDA). In [47], the authors used a lightweight Bayesian Estimator in sensor nodes. At the base station, they used Naive Bayes-, SVM-, and MLP-based inference systems to generate decisions based on the estimated data sent by the sensor nodes. The authors in [48] propose an autonomous prediction algorithm for heterogeneous applications in WSNs. The algorithm is stated in steps, which can be configured to different problems. It uses a combination of correlation analysis, SARIMA to forecast time series, and then a Multiple Linear Regression Model layer. The main advantage of the algorithm is the ability to adapt to different applications. In [49], the authors proposed a damage prediction system for wind turbines, named Delphos, based on WSN and an actuator network. The system predicts the state of a turbine to know when it is going to be damaged and take action before it happens. It is based on the ARIMA method to forecast the state of the turbine and a fuzzy system to filter the effect of environmental temperatures on the prediction. The authors concluded that when removing the effect of temperature, predictions of damage are very accurate.