Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations
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 da...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2018-03-01
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| Series: | EAI Endorsed Transactions on Internet of Things |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.20-12-2018.156083 |
| _version_ | 1818505623271112704 |
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| author | Mary Nsabagwa Julianne Otim Roseline Akol Grace Ninsiima Robert Mwesigye Maximus Byamukama Björn Pehrson |
| author_facet | Mary Nsabagwa Julianne Otim Roseline Akol Grace Ninsiima Robert Mwesigye Maximus Byamukama Björn Pehrson |
| author_sort | Mary Nsabagwa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T21:53:24Z |
| format | Article |
| id | doaj.art-ffacfbd01c2140b98529de9227265f8b |
| institution | Directory Open Access Journal |
| issn | 2414-1399 |
| language | English |
| last_indexed | 2024-12-10T21:53:24Z |
| publishDate | 2018-03-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Internet of Things |
| spelling | doaj.art-ffacfbd01c2140b98529de9227265f8b2022-12-22T01:32:08ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Internet of Things2414-13992018-03-0141410.4108/eai.20-12-2018.156083Condition Monitoring for Wireless Sensor Network-Based Automatic Weather StationsMary Nsabagwa0Julianne Otim1Roseline Akol2Grace Ninsiima3Robert Mwesigye4Maximus Byamukama5Björn Pehrson6Department of Networks, Makerere University, Kampala, UgandaDepartment of Networks, Makerere University, Kampala, Uganda Department of Electrical & Computer EngineeringDepartment of Networks, Makerere University, Kampala, UgandaDepartment of Networks, Makerere University, Kampala, Uganda Department of Electrical & Computer EngineeringKTH Royal Institute of Technology, Stockholm, SwedenWireless 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.https://eudl.eu/pdf/10.4108/eai.20-12-2018.156083Automatic Weather Station (AWS)condition monitoringqueuingWireless Sensor Networks |
| spellingShingle | Mary Nsabagwa Julianne Otim Roseline Akol Grace Ninsiima Robert Mwesigye Maximus Byamukama Björn Pehrson Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations EAI Endorsed Transactions on Internet of Things Automatic Weather Station (AWS) condition monitoring queuing Wireless Sensor Networks |
| title | Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations |
| title_full | Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations |
| title_fullStr | Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations |
| title_full_unstemmed | Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations |
| title_short | Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations |
| title_sort | condition monitoring for wireless sensor network based automatic weather stations |
| topic | Automatic Weather Station (AWS) condition monitoring queuing Wireless Sensor Networks |
| url | https://eudl.eu/pdf/10.4108/eai.20-12-2018.156083 |
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