Optimal Node Selection using Estimated Data Accuracy Model in Wireless Sensor Networks

TL;DR

This paper introduces an estimated data accuracy model for wireless sensor networks that improves data quality and enables optimal node selection, even in the presence of malicious nodes, outperforming existing models.

Contribution

The paper presents a novel estimated data accuracy model and a probabilistic node selection method that enhances data accuracy and robustness in wireless sensor networks.

Findings

The proposed model outperforms existing accuracy models.

The model maintains accuracy even with malicious nodes.

Optimal node selection improves overall data quality.

Abstract

One of the major task of wireless sensor network is to sense accurate data from the physical environment. Hence in this paper, we develop an estimated data accuracy model for randomly deployed sensor nodes which can sense more accurate data from the physical environment. We compare our results with other information accuracy models and shows that our estimated data accuracy model performs better than the other models. Moreover we simulate our estimated data accuracy model under such situation when some of the sensor nodes become malicious due to extreme physical environment. Finally using our estimated data accuracy model we construct a probabilistic approach for selecting an optimal set of sensor nodes from the randomly deployed maximal set of sensor nodes in the network.