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

Abstract

Background: Data prediction methods in wireless sensor networks (WSN) has been 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 involves increased data transmissions. Method: Here, we develop 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 ang 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 that 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.

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

“…Past studies and research concentrated on data prediction models and techniques for each SN stage, CH stage as well as BS stage in which high memory, data processing, and delay control methods are required. As a result, concerning future directions, there is still a need to emphasize data prediction models to reduce the cost of redundant data transmission and data delay 12,13,59,62,63,67,76,87,90,93,109–117,121,136 …”

“…Energy‐efficient redundant data transmission reduction is required for scaling such WSN applications. As a result, achieving a longer network lifetime is still considered the most difficult issue in the research 12,13,57–65,67,79,83–94,109–112,114–117,121 …”

“…As a result, more research is required to fine‐tune the precise amount of redundant data transmissions. Further research is still recommended despite the improvements in data accuracy 41,66,67,83–84,87,90–93,111–113,115–117,121 …”

“…As a result, achieving a longer network lifetime is still considered the most difficult issue in the research. 12,13,[57][58][59][60][61][62][63][64][65]67,79,[83][84][85][86][87][88][89][90][91][92][93][94][109][110][111][112][114][115][116][117]121…”

“…As a result, concerning future directions, there is still a need to emphasize data prediction models to reduce the cost of redundant data transmission and data delay. 12,13,59,62,63,67,76,87,90,93,[109][110][111][112][113][114][115][116][117]121,136…”