Detecting anomalous sensor readings in wireless networks for remote area

Abstract

In recent years, the use of wireless sensor networks has become increasingly popular in remote areas for various applications, including environmental monitoring and surveillance. However, wireless sensor networks are often vulnerable to anomalous sensor readings, which can be caused by various factors, such as hardware malfunctions, environmental changes, and interference from other sources. Anomalous sensor readings can lead to false alarms, reduced data quality, and decreased network reliability. To address this challenge, this paper presents a study of anomalous sensor reading detection in wireless networks for remote areas. Our approach is based on machine learning algorithms and involves the use of a clustering algorithm to identify normal patterns in the sensor readings, and a classifier to detect readings that deviate from these patterns. We evaluate the performance of our approach using a dataset of real-world sensor readings and compare it to several benchmark methods. The results of our study demonstrate that our approach outperforms the benchmark methods in terms of accuracy and robustness, and that it is capable of effectively detecting anomalous sensor readings. Furthermore, our approach has the advantage of being scalable and easily adaptable to different types of sensor readings and applications. In conclusion, our approach provides a promising solution for the detection of anomalous sensor readings in wireless networks for remote areas. The results of this study have the potential to improve the reliability and performance of wireless sensor networks and support the development of more effective and efficient monitoring systems for remote areas. We hope that this work will inspire further research in the field and contribute to the development of advanced techniques for anomalous sensor reading detection.