A Two Vector Data-Prediction Model for Energy-Efficient Data Aggregation in Wireless Sensor Network

Abstract

Most ecological management applications use Wireless Sensor Networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two-vector prediction model based on Normalized Quantile Regression (NQR) for Data Aggregation is proposed to proficiently accomplish energy reduction in synchronized data collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates, energy usage is reduced.Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two-vector data-prediction algorithm to coordinate the anticipated sensor's reading and, as a result, minimize cumulative inefficiencies from unin-terrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to existing methods, the suggested NQR methodology is shown to have high energy efficiency.The results show greater prediction accuracy, more positive predictions with high data quality, which help the network last longer.

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

“…An algorithm's complexity is define as how the algorithm performs in different conditions. It is expressed numerically as a function of 𝑇(𝑛) time versus 𝑛 input size [37]. Here we have estimated the algorithm's efficiency asymptotically.…”

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

“…ST‐DAM lacks stability and scalability of the model. Jain and Singh 93 in 2022 proposed a normalized quantile regression (NQR) model as a large amount of energy is consumed in transmitting redundant and highly correlated data. NQR employs a two‐vector data prediction model to bring high prediction accuracy with reduced historic values.…”

“…Although there is no SE or prediction threshold value defined for any data shape and size, making it is difficult to compare research with different applications. It is more difficult to establish control and termination data‐links 57–60,63–66,87,90–91,93,112–113 …”

“…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…”

“…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 …”