Anomaly detection in engineering structures using WSN and machine learning

Abstract

Wireless Sensor Networks are widely used for data acquisition in wide areas of applications like Health care, agriculture, surveillance etc. MEMs technology enables development of highly efficient, minute sensors. One of such applications of Wireless Sensor Networks (WSN) is monitoring the engineering structures, for damage detection and characterization. WSN technology is used for detecting the level of damage in huge bridge structures in metros and cities. Various technologies like wi-fi, zigbee, Bluetooth etc are used in the existing system for communication between nodes of the WSN. A novel method using RF technology for WSN is proposed that enables the coverage of a large area and higher data transfer speed. Novel methods of data analysis using machine learning also needs to be explored, to generate incites to the huge amount of data generated by sensors. Localization or finding the exact location of the problem area in the sensor network is a tedious task and can be handled well by using machine learning algorithms.