A Combinational Data Prediction Model for Data Transmission Reduction in Wireless Sensor Networks

Background: Data prediction methods in wireless sensor networks (WSN) has emerged as a significant way to reduce the redundant data transfers and in extending the overall network's lifetime. Nowadays, two types of data prediction algorithms are in use. The first focus on reassembling historical data and providing backward models, resulting in unmanageable delays. The second is concerned with future data forecasting and gives forward models, that involve increased data transmissions. Method:Here, we developed a Combinational Data Prediction Model (CDPM) that can build prior data to control delays as well as anticipate future data to reduce excessive data transmission. To implement this paradigm in WSN applications two algorithms are implemented. The first algorithm creates step-by-step optimal models for sensor nodes (SNs). The other predicts and regenerates readings of the sensed data by the base stations (BS). Comparison: To evaluate the performance of our proposed CDPM data-prediction method, a WSN-based real application is simulated using a real data set. The performance of CDPM is also compared with HLMS, ELR, and P-PDA algorithms. Results:The CDPM model displayed significant transmission suppression (16.49%, 19.51% and 20.57%%), reduced energy consumption (29.56%, 50.14%, 61.12%) and improved accuracy (15.38%, 21.42%, 31.25%) when compared with HLMS, ELR and P-PDA algorithms respectively. The delay caused by CDPM training is also controllable in data collection. Conclusion: Results advised the efficacy of the proposed CDPM over a single forward or backward model in terms of decreased data transmission, improved energy efficiency, and regulated latency.