Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations

Abstract

Wireless Sensor Network (WSN)-based Automatic Weather Stations (AWSs) perform automatic collection and transmission of weather data. These AWSs face challenges, which lower their performance. Hence, a need for regular monitoring to reduce down time. We propose condition monitoring, comprised of a data receiver, analyser, problem classifier and reporter and visualizer, to mine data relationships, identify possible causes of problems and perform reporting of AWS status. The data receiver uses an M/M/1/k queuing model. We use Successive Pairwise REcord Differences (SPREDs) algorithm to compare arrival rates and packet content so as to establish sensor, node and AWS level performance. We also perform a hybrid of Grubb outlier detection and correlations amongst related variables for data validation. Problems take on one of four states. One connection can receive data at a rate as low as 1ms, without loss while problem identification especially in high density network is improved.