Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets
Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research propos...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/12/9/1391 |
| _version_ | 1797556571386937344 |
|---|---|
| author | Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira |
| author_facet | Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira |
| author_sort | Emmanuel Lule |
| collection | DOAJ |
| description | Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO<sub>2</sub> and O<sub>2</sub> vis-à-vis the Estimated Fire Intensity Prediction (<b>EFIP</b>). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places. |
| first_indexed | 2024-03-10T17:04:49Z |
| format | Article |
| id | doaj.art-6349d3760db047c2b0fb7d82cd4bdba9 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T17:04:49Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-6349d3760db047c2b0fb7d82cd4bdba92023-11-20T10:51:14ZengMDPI AGSymmetry2073-89942020-08-01129139110.3390/sym12091391Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban MarketsEmmanuel Lule0Chomora Mikeka1Alexander Ngenzi2Didacienne Mukanyiligira3African Center of Excellence in Internet of Things (ACEIoT), College of Science & Technology (C.S.T.), University of Rwanda, Nyarugenge P.O. Box 3900, Kigali, RwandaChancellor College, Faculty of Science, Department of Physics, University of Malawi (UNIMA), Zomba P.O. Box 280, MalawiAfrican Center of Excellence in Internet of Things (ACEIoT), College of Science & Technology (C.S.T.), University of Rwanda, Nyarugenge P.O. Box 3900, Kigali, RwandaAfrican Center of Excellence in Internet of Things (ACEIoT), College of Science & Technology (C.S.T.), University of Rwanda, Nyarugenge P.O. Box 3900, Kigali, RwandaFire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO<sub>2</sub> and O<sub>2</sub> vis-à-vis the Estimated Fire Intensity Prediction (<b>EFIP</b>). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.https://www.mdpi.com/2073-8994/12/9/1391Internet of Things (IoT)fuzzy logicFuzzy Associative Memory (<b>FAM</b>)estimated fire intensity prediction (<b>EFIP</b>)gas combustion efficiency (<b>GCE</b>) |
| spellingShingle | Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets Symmetry Internet of Things (IoT) fuzzy logic Fuzzy Associative Memory (<b>FAM</b>) estimated fire intensity prediction (<b>EFIP</b>) gas combustion efficiency (<b>GCE</b>) |
| title | Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets |
| title_full | Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets |
| title_fullStr | Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets |
| title_full_unstemmed | Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets |
| title_short | Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets |
| title_sort | design of an iot based fuzzy approximation prediction model for early fire detection to aid public safety and control in the local urban markets |
| topic | Internet of Things (IoT) fuzzy logic Fuzzy Associative Memory (<b>FAM</b>) estimated fire intensity prediction (<b>EFIP</b>) gas combustion efficiency (<b>GCE</b>) |
| url | https://www.mdpi.com/2073-8994/12/9/1391 |
| work_keys_str_mv | AT emmanuellule designofaniotbasedfuzzyapproximationpredictionmodelforearlyfiredetectiontoaidpublicsafetyandcontrolinthelocalurbanmarkets AT chomoramikeka designofaniotbasedfuzzyapproximationpredictionmodelforearlyfiredetectiontoaidpublicsafetyandcontrolinthelocalurbanmarkets AT alexanderngenzi designofaniotbasedfuzzyapproximationpredictionmodelforearlyfiredetectiontoaidpublicsafetyandcontrolinthelocalurbanmarkets AT didaciennemukanyiligira designofaniotbasedfuzzyapproximationpredictionmodelforearlyfiredetectiontoaidpublicsafetyandcontrolinthelocalurbanmarkets |