Research Area:  Wireless Sensor Networks
Most environmental monitoring application periodically senses and aggregated data by sensor networks which usually exhibits high temporal redundancies. An enormous amount of energy is depleted in transmitting this redundant information making it extremely difficult to achieve an acceptable network lifespan, which has become a bottleneck in scaling such applications. To efficiently manage the energy depletion in concurrent data collection rounds, a prediction model based on Extended Cosine Regression (ECR) for Data Aggregation is proposed. The proposed technique delivers prediction with high accuracy and the energy consumption is minimized with successful predictions and thereby increases the data cycles and network lifetime. ECR also uses a two-vector model in the intra-cluster transmissions to synchronize the predicted data values and therefore minimizes cumulative errors from continuous predictions. The proposed ECR technique is simulated using NS2-34 shows high-energy efficiency as compared with the existing schemes. Results demonstrate high prediction accuracy, a number of successful predictions and a lesser degree of prediction errors, which obviously improve the networks lifetime.
Author(s) Name:  Khushboo Jain & Anoop Kumar
Journal name:  Journal of Ambient Intelligence and Humanized Computing
Publisher name:  Springer
Volume Information:  volume 11, pages 5205–5216 (2020)
Paper Link:   https://link.springer.com/article/10.1007/s12652-020-01833-2