Research Article
Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations
@ARTICLE{10.4108/eai.20-12-2018.156083, author={Mary Nsabagwa and Julianne Sansa Otim and Roseline Nyongarwizi Akol and Grace Ninsiima and Robert Mwesigye and Maximus Byamukama and Bj\o{}rn Pehrson}, title={Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations}, journal={EAI Endorsed Transactions on Internet of Things}, volume={4}, number={14}, publisher={EAI}, journal_a={IOT}, year={2018}, month={3}, keywords={Automatic Weather Station (AWS), condition monitoring, queuing, Wireless Sensor Networks}, doi={10.4108/eai.20-12-2018.156083} }- Mary Nsabagwa
Julianne Sansa Otim
Roseline Nyongarwizi Akol
Grace Ninsiima
Robert Mwesigye
Maximus Byamukama
Björn Pehrson
Year: 2018
Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations
IOT
EAI
DOI: 10.4108/eai.20-12-2018.156083
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.
Copyright © 2018 Mary Nsabagwa et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.