Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets
Abstract
:1. Introduction
2. Related Works
3. Problem Description and Modeling
3.1. Problem Definition
3.2. Materials and Methods
3.3. Fuzzy Approximation Modeling
Fuzzy Logic
- It models uncertainty of linear and nonlinear systems of arbitrary complexity to solve real-world complex dynamic computational problems;
- It covers a range of operating conditions, and is readily customizable in natural language processing terms;
- It exhibits the ability to handle dynamic complex problems with imprecise and incomplete datasets;
- It exhibits a great sense of flexibility and simplicity in modeling real life complex problems.
3.4. Fuzzy Approximation Control Model
3.4.1. The Fuzzy Control Model
3.4.2. Model Fuzzification Values
3.4.3. Used Membership Functions
3.5. Applied Model Fuzzy Rules
3.5.1. Fuzzy Associative Memory (FAM) Method
3.5.2. Fuzzy Inference Evaluation Rules for the Control Experiment Design
3.6. Model Fuzzy Inference System
3.7. Model Defuzzification and Evaluation
3.8. Architectural Design of Fire Detection Model
Simulation Input and Output Fuzzy Parameters Considered
4. Simulation Experimental Setup
4.1. The Fuzzy Control System (FCS) Design
4.2. Input/Output Fuzzy Membership Functions Designs
4.3. MATLAB Evaluation Rules Editor Proposed Fuzzy Based Model
4.4. MATLAB Evaluation Rules Viewer
4.5. Proposed IoT-Based Fuzzy Approximation Prediction Model
5. Results and Discussions
5.1. Initial Environmental Parameters, i.e., Temperature, Humidity and Flame vs EFIP
5.2. Gas Combustion i.e., CO, CO2, O2 and Flame vs EFIP
5.3. Simulation Experiment Data Results
5.4. Gas Combustion Efficiency (GCE)
5.5. Model Performance Evaluation
6. Conclusion and Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
API | Application Programming Interface |
ANP | Analytical Network Processing |
ANFIS | Adaptive Neural Fuzzy Inference System |
CNN | Convolutional Neural Networks |
CO | Carbon monoxide |
CO2 | Carbon dioxide |
EA | East Africa |
EFIP | Estimated Fire Intensity Prediction |
FAM | Fuzzy Associative Memory |
FCS | Fuzzy Control System |
FIS | Fuzzy Inference System |
FMWS | Fire Monitoring Warning System |
GCE | Gas Combustion Efficiency |
GIS | Geographical Information System |
GSM | Global System for Mobile Communication |
IDE | Integrated Development Environment |
IoT | Internet of Things |
O2 | Oxygen |
MCU | Micro Controller Unit |
MEM | Micro Electro Mechanical |
MF | Membership Function |
MQTT | Message Queue Telemetry Transport |
TSK | Takagi, Sugeno, Tangi |
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Rule No. | Temperature (°C) (ΔT) | Humidity (%) (ΔH) | Estimated Fire Intensity Prediction (EFIP) (%) |
---|---|---|---|
1. | Very Low | Dry | L |
2. | Very Low | Optimal | L |
3. | Very Low | Moist | L |
4. | Low | Dry | L |
5. | Low | Optimal | L |
6. | Low | Moist | L |
7. | Medium | Dry | M |
8. | Medium | Optimal | M |
9. | Medium | Moist | L |
10. | High | Dry | H |
11. | High | Optimal | H |
12. | High | Moist | L |
13. | Very High | Dry | H |
14. | Very High | Optimal | H |
15. | Very High | Moist | L |
Rule No. | Smoke Intensity (ΔCO) (ppmv) | Carbon Dioxide (ΔCO2) (ppmv) | Oxygen Level (ΔO2) (ppmv) | Estimated Fire Intensity Prediction (EFIP) (%) |
---|---|---|---|---|
1. | Low | Low | Low | L |
2. | Low | Medium | Low | M |
3. | Low | High | Low | H |
4. | Medium | Low | Low | H |
5. | Medium | Medium | Low | VH |
6. | Medium | High | Low | VH |
7. | High | Low | Low | VH |
8. | High | Medium | Low | VH |
9. | High | High | Low | VH |
10. | Low | Low | Medium | VL |
11. | Low | Medium | Medium | M |
12. | Low | High | Medium | M |
13. | Medium | Low | Medium | M |
14. | Medium | Medium | Medium | H |
15. | Medium | High | Medium | L |
16. | High | Low | Medium | M |
17. | High | Medium | Medium | H |
18. | High | High | Medium | L |
19. | Low | Low | High | VL |
20. | Low | Medium | High | VL |
21. | Low | High | High | H |
22. | Medium | Low | High | VL |
23. | Medium | Medium | High | L |
24. | Medium | High | High | L |
25. | High | Low | High | L |
26. | High | Medium | High | L |
27. | High | High | High | L |
Crisp Input Variable. | Fuzzy Input Parameters. | Fuzzy Domain Range. | Universe of Discourse for MFs |
---|---|---|---|
Temperature(ΔT) | Very Low, Low, Medium, High, Very High. | [0–100] | 0–20, 20–40, 40–60, 60–80 and 80–100 respectively. |
Humidity (ΔH) | Dry, Optimal, Moist. | [0–100]- (%) | 0–40, 40–80, 80–100 |
Smoke (CO) | Low, Medium, High. | [0–100] | 0–40, 40–80, 80–100 |
Carbon dioxide (CO2) | Low, Medium, High. | [0–100] | 0–40, 40–80, 80–100 |
Oxygen Level (O2) | Low, Medium, High. | [0–100] | 0–40, 40–80, 80–100 |
Flame Presence | Boolean: False, True. | [False, True] | 0, 1 |
Crisp Output Variable. | Fuzzy Output Parameters | Fuzzy Domain Range. | Universe of Discourse for MFs |
---|---|---|---|
Estimated Fire Intensity Prediction(EFIP)% | Very Low, Low Moderate, High, Very High. | [0–100]- (%) | 0–20, 20–40, 40–60, 60–80 and 80–100 respectively. |
Rule No. | ΔT(°C) | ΔH (%) | EFIP (%) |
---|---|---|---|
1. | 9.0 | 17.5 | 17.6 |
2. | 15.1 | 27.1 | 18.3 |
3. | 38.0 | 58.4 | 43.6 |
4. | 50.0 | 50.0 | 52.1 |
5. | 28.3 | 38.0 | 22.9 |
6. | 40.4 | 44.0 | 46.2 |
7. | 46.4 | 48.8 | 52.1 |
8. | 52.4 | 65.7 | 51.9 |
9. | 62.9 | 71.7 | 54.5 |
10. | 66.9 | 80.1 | 37.9 |
11. | 68.1 | 52.4 | 71.2 |
12. | 68.1 | 22.3 | 69.7 |
Rule No. | ΔCO | ΔCO2 | ΔO2 | EFIP (%) |
---|---|---|---|---|
1. | 23.6 | 21.4 | 23.6 | 47.8 |
2. | 32.4 | 29.1 | 28.0 | 63.2 |
3. | 36.8 | 30.2 | 31.1 | 69.1 |
4. | 44.5 | 32.4 | 39.0 | 71.3 |
5. | 50.0 | 37.9 | 44.5 | 75.0 |
6. | 19.2 | 48.9 | 51.1 | 56.5 |
7. | 13.7 | 8.2 | 13.7 | 25.0 |
8. | 64.3 | 50.0 | 17.0 | 86.5 |
9. | 70.9 | 24.7 | 32.4 | 62.8 |
10. | 32.4 | 46.7 | 65.4 | 70.3 |
11. | 48.9 | 75.3 | 86.3 | 25.0 |
12. | 57.7 | 69.8 | 62.1 | 60.9 |
Expt. No. | ΔT(°C) | ΔH (%) | Flame Presence | EFIP (%) | Test Model | Actual Model | Determined Accuracy Rate (%) |
---|---|---|---|---|---|---|---|
1. | 9.0 | 17.5 | True | 17.6 | L | VL | 50% |
2. | 15.1 | 27.1 | True | 18.3 | L | VL | 50% |
3. | 38.0 | 58.4 | True | 43.6 | M | M | 100% |
4. | 50.0 | 50.0 | True | 52.1 | M | M | 100% |
5. | 28.3 | 38.0 | True | 22.9 | L | L | 100% |
6. | 40.4 | 44.0 | True | 46.2 | M | M | 100% |
7. | 46.4 | 48.8 | True | 52.1 | M | M | 100% |
8. | 52.4 | 65.7 | True | 51.9 | M | M | 100% |
9. | 62.9 | 71.7 | True | 54.5 | M | M | 100% |
10. | 66.9 | 80.1 | True | 37.9 | L | L | 100% |
11. | 68.1 | 52.4 | True | 71.2 | H | H | 100% |
12. | 68.1 | 22.3 | True | 69.7 | H | H | 100% |
Expt. No. | ΔCO(ppm) | ΔCO2(ppm) | ΔO2(ppm) | Flame Presence | EFIP (%) | Test Model | Actual Model | Determined Accuracy Rate (%) |
---|---|---|---|---|---|---|---|---|
1. | 23.6 | 21.4 | 23.6 | True | 47.8 | M | M | 100% |
2. | 32.4 | 29.1 | 28.0 | True | 63.2 | H | H | 100% |
3. | 36.8 | 30.2 | 31.1 | True | 69.1 | H | H | 100% |
4. | 44.5 | 32.4 | 39.0 | True | 71.3 | H | H | 100% |
5. | 50.0 | 37.9 | 44.5 | True | 75.0 | H | H | 100% |
6. | 19.2 | 48.9 | 51.1 | True | 56.5 | M | M | 100% |
7. | 13.7 | 8.2 | 13.7 | True | 25.0 | L | L | 100% |
8. | 64.3 | 50.0 | 17.0 | True | 86.5 | VH | VH | 100% |
9. | 70.9 | 24.7 | 32.4 | True | 62.8 | H | H | 100% |
10. | 32.4 | 46.7 | 65.4 | True | 70.3 | H | H | 100% |
11. | 48.9 | 75.3 | 86.3 | True | 25.0 | L | L | 100% |
12. | 57.7 | 69.8 | 62.1 | True | 60.9 | H | H | 100% |
Features. | Kaur et al. (2019) [18] | Sawar et al. (2018) [15] | Abedi et al. (2019) [13] | Sowah et al. (2020) [7] | Proposed Solution |
---|---|---|---|---|---|
Multisensor parameters | Four inputs: temperature, humidity, smoke and flame | Yes. Four input parameters: temperature, humidity, time and flame. | Yes. Three input parameters: smoke, temperature and humidity. | Yes. Four input parameters, smoke, temperature, humidity and flame. | Yes. Six input parameters: temperature, humidity, CO, CO2, O2 and flame. |
Method or technique | K-means clustering, adaptive ANFIS | Single simulated expt. model using fuzzy logic method | Analytical network processing (ANP), fuzzy logic | Fuzzy logic method, trained CNN, a deep learning technique | Two separate integrated simulated expt. models using fuzzy logic method |
Accuracy rate (%) | 93.12% | 95.8% | 81.9% | 94.0% | 95.83% |
False alarm detection | Sends early warning signals | Yes. Notification warnings | Generates a forest fire risk map | Yes. Web notification platform | Yes. Determination of fire intensity status notifications followed with appropriate action. |
Decision on two authentications | No | Yes | No | No | Yes |
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Share and Cite
Lule, E.; Mikeka, C.; Ngenzi, A.; Mukanyiligira, D. 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 2020, 12, 1391. https://doi.org/10.3390/sym12091391
Lule E, Mikeka C, Ngenzi A, Mukanyiligira D. 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. 2020; 12(9):1391. https://doi.org/10.3390/sym12091391
Chicago/Turabian StyleLule, Emmanuel, Chomora Mikeka, Alexander Ngenzi, and Didacienne Mukanyiligira. 2020. "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 12, no. 9: 1391. https://doi.org/10.3390/sym12091391