## 3 Data Prediction for Energy Saving in WSNs
### Related Works to Data Reduction for Energy Saving in WSNs  
Many researches have addressed the problem of transmissions reduction in WSNs to prolong the lifetime of the sensors. In [35], the authors proposed a BS-assisted cluster, spatially correlated with the rest of the nodes (in a SPS) gathering data and using compressive sensing to reduce the number of transmissions. Authors in [36] provided another example of SPS in which they proposed another correlation-based approach for cluster-based networks. In this case, the CH computes the correlation between the measured data and adjusts the sampling rate of the sensors accordingly. Then a prediction algorithm is run in the BS to reconstruct the missing points. The proposed algorithm shows a reduction in energy of up to 60%. A single prediction scheme based on an ANN in a DPS and a request management algorithm was proposed [37]. The authors achieved a relatively good performance of around 90% in reducing energy consumption in the sensor node, obtaining a regression fit of 0.99 after training 50 hidden layers for 1000 epochs. However, the algorithm was tested with relatively easy-to-model data (cyclic, sinusoidal, and smooth data). Figure 11: Dual prediction scheme operating on a Wireless Sensor Network. In [38], the authors proposed an adaptive sampling algorithm that estimates the best sampling frequency for the sensors dynamically. Another use of this approach is for object tracking WSNs. An example can be found in [39], where the authors proposed a prediction-based tracking technique using a sequential pattern (PTSP) for object tracking WSNs in a SPS. The model is computed by the Sink, and the predictions occur only in the sensors. This reduces the transmissions in the WSN. Sensors predict the future movements of the objects, keeping the sensors outside their range in sleep mode. Since it is fully SPS, it depends fully on the accuracy of the prediction, and it is possible to have some missing objects during the tracking process. The algorithm maintains an acceptable number of missing points, outperforming the rest of the compared object-tracking sensor network (OTSN) algorithms in reduction of energy. In [40], the authors proposed an Adaptive Method for Data Reduction in a DPS using an Independent Model Generation scheme based on Least-Mean-Square (LMS) adaptive filters. The algorithm provides a communication reduction up to 95% with a predefined threshold in a real-world temperature dataset with relatively good accuracy. Authors in [41] used an autoregressive method named SETAR to save energy in a WSN measuring electric engine vibrations in a dual prediction framework. The algorithm is easy to implement, and therefore, it can be used by most microcontrollers. It was tested on an electric engine vibration dataset collected from a real factory environment with complex behavior. The algorithm achieved energy saving of up to 73% using a relatively simple algorithm (AR models). Another autoregressive method was presented in [6] also in a dual prediction framework to predict humidity data. The algorithm requires computing the \(p\)-value used for choosing the appropriate order of the AR model that can be determined by using the Partial Autocorrelation Function (PACF). The results show a reduction in transmission between the sensor node and base stations up to 85% using different thresholds. In [42], the authors presented a DPS based on a Hierarchical Least-Mean-Square (HLMS) adaptive filter. The simulation results show that the proposed scheme achieves up to 95% communication reduction for the temperature measurements maintaining an error of 0.3 \({}^{\circ}\)C to 1 \({}^{\circ}\)C. However, the results presented a high bit error rate (BER), channel condition fluctuation, and hand-off in a wireless link causing packet loss in transmissions. In [43], the authors presented a data compression scheme based on fog computing architecture.