Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention

Front Cover
Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention
Copyright
Dedication
Contents
Contributors
Preface
Section 1: Nanotechnology for disaster prevention
Chapter 1: Application of nanotechnology in disaster prevention: An introduction
1.1. Introduction
1.2. Nanotechnology in sustainable agriculture and hunger prevention
1.3. Role of nanotechnology in environmental pollution prevention
1.4. Nanotechnology in harvesting renewable energy
1.5. Nanotechnology in health sector
1.6. Nanotechnology in protection of infrastructures
1.7. Conclusion
References
Chapter 2: Nanomaterials for construction building products designed to withstand natural disasters
2.1. Introduction
2.2. Nanomaterials used in the construction
2.3. Traditional materials for construction against disaster
2.4. Sustainable usages of nano-based materials
2.4.1. Nanoceramic coating
2.4.2. Nano fibers
2.4.3. Nanocomposites
2.4.4. Nanoclays
2.4.5. Titanium dioxide
2.4.6. Carbon nanotubes
2.4.7. Elctrochromic windows
2.4.8. MMFX2 steel
2.4.9. Nanowire
2.4.10. Nanosilica
2.5. Nanomaterials in advanced architecture
2.6. Health aspects of nanomaterials when used in the construction building materials
2.7. Environmental advantages and disadvantages and life-cycle assessment
2.8. Risk assessment and analysis for nanomaterials used in the construction
2.9. Regulations data in various countries
2.9.1. United States
2.9.2. Australia
2.9.3. Europe
2.9.4. China
2.10. Conclusion
2.11. Future scope
References
Chapter 3: Nano-sensors and nano-devices for biological disaster monitoring (virus/disease epidemics/animal plagu
3.1. Introduction
3.2. Nano-sensors and nano-devices
3.3. The biological disaster monitoring applications
3.3.1. Viruses
3.3.1.1. Human immunodeficiency virus
3.3.1.2. Human papilloma virus
3.3.1.3. SARS virus
3.3.1.4. Ebola virus
3.3.1.5. Zika virus
3.3.1.6. Hepatitis
3.3.1.7. Influenza virus
3.4. Conclusions
References
Chapter 4: Internet of Things-based disaster management system
4.1. Introduction to disaster
4.2. Classification
4.3. Wireless sensor network and internet of things
4.3.1. IoT
4.3.2. Sensors used for disaster management
4.4. Design challenges of using WSN/IoT in disaster management and possible solutions
4.4.1. Deployment strategy
4.4.2. IoT system
4.4.2.1. Hardware
Edge
Network
Core
Date integration
Data management
Business innovation
4.4.2.2. IoT-supported protocols for disaster management
4.5. Results and discussion
4.5.1. Landslide
4.5.2. Earthquake disaster management
4.5.3. Fire detection alarm using IoT
4.5.4. Industrial disaster management
4.5.5. Urban disaster management
4.6. Scope for research in disaster management
4.6.1. Cost
4.6.2. Energy
4.6.3. Interoperability
4.6.4. Maintenance
4.6.5. Robust and fault tolerance
4.6.6. Minimize computation
4.6.7. Artificial intelligence
4.6.8. Data
4.7. Conclusion
References
Chapter 5: Nanosensors for smartphone-enabled sensing devices
5.1. Introduction
5.2. Nanosensors
5.2.1. Smart sensing system
5.3. Nanosensors on smart platforms
5.3.1. Optical nanosensors on smartphone sensing
5.3.2. Mass-based nanosensors on smartphone sensing
5.3.3. Electrochemical nanosensors on smartphone sensing
5.4. Conclusion and future perspectives
References
Chapter 6: Smart and autonomous (self-powered) nanosensor networks
6.1. Introduction
6.2. Technology for self-powered nanosensors
6.3. Applications of self-powered sensors for natural disasters
6.4. Conclusion and remarks
References
Chapter 7: Nanosensors for smartphone sensing method
7.1. Introduction
7.2. Applications of nanosensors in smartphones
7.2.1. Energy autonomy
7.2.2. Physical durability
7.3. Conclusion and remarks
References
Section 2: Space technology for disaster prevention
Chapter 8: Nanotechnology in the space industry
8.1. Introduction of nanotechnology in space technology
8.2. Nanomaterials in space industries
8.2.1. Carbon nanotubes
8.2.2. Nano Ti alloys
8.2.3. Nano composites
8.3. Nanostructures in aero-parts
8.3.1. Nanosensors
8.3.2. Thin solar sails
8.3.3. Nanofuel in propulsion systems
8.3.4. CNT-wheels
8.3.5. Aero-vehicle frames
8.4. Summary
References
Chapter 9: Unmanned aerial vehicles (UAVs) for disaster management
9.1. Introduction
9.2. UAV advancement for disaster management
9.2.1. UAV services
9.3. UAV-assisted communication network for disaster management
9.3.1. UAV-assisted network architecture for disaster management
9.3.2. UAV-assisted network design considerations
9.4. Disaster types and phases
9.4.1. Type A disasters: Massive terrestrial infrastructure damage
9.4.2. Type B disasters: Moderate terrestrial infrastructure damage
9.4.3. Type C disaster: Low terrestrial infrastructure damage
9.5. Case studies
9.5.1. Case study 1: UAV-assisted earthquake response and recovery
9.5.2. Case study 2: Wildfire detection and monitoring
9.5.3. Case study 3: UAV-assisted biological diseases management
9.6. Conclusions
References
Chapter 10: The role of satellite remote sensing in natural disaster management
10.1. Introduction
10.1.1. Hydrological and geological hazards
10.1.2. Geophysical hazards
10.1.3. Meteorological hazards
10.1.4. Climatological hazards
10.2. Remote sensing data and techniques to access natural disasters
10.2.1. Hydrological and geological hazards
10.2.1.1. Floods
10.2.1.2. Landslides
10.2.1.3. Sea-level rise
10.2.2. Geophysical hazards
10.2.2.1. Earthquakes
10.2.2.2. Volcanoes
10.2.2.3. Tsunamis
10.2.3. Meteorological hazards
10.2.3.1. Storms
10.2.3.2. Tropical cyclones
10.2.4. Climatological hazards
10.2.4.1. Droughts
10.2.4.2. Fires
10.2.4.3. Desertification
10.2.4.4. Coastal erosion
10.3. Conclusions
References
Chapter 11: The synergy of remote sensing and geographical information systems in the management of natural disasters
11.1. Introduction
11.2. The synergy of remote sensing and GIS in the management of natural disasters
11.2.1. Hydrological and geological hazards
11.2.2. Geophysical hazards
11.2.3. Meteorological hazards
11.2.4. Climatological hazards
11.3. Conclusions
References
Chapter 12: Small satellites for disaster monitoring
12.1. Introduction
12.2. Remote sensing platforms
12.2.1. Mission related aspects
12.2.1.1. Orbits and temporal resolution
12.2.1.2. Spatial resolution
12.2.1.3. Necessary infrastructure
12.3. A taxonomy of disasters
12.4. Enabling technologies
12.4.1. Instruments
12.4.2. Constellations
12.4.3. Ground segment
12.4.4. Data collection systems
12.5. Conclusions
References
Chapter 13: A comparative study of deep learning-based time-series forecasting techniques for fine-scale&spi
13.1. Introduction
13.2. Data
13.2.1. IoT air temperature from GeoTab
13.2.2. Air temperature measurements from weather underground
13.2.3. High-resolution rapid refresh (HRRR)
13.3. Methods
13.3.1. Stacked LSTM
13.3.2. ConvGRU
13.3.3. Transformer
13.4. Training and evaluation
13.4.1. Training and testing data split
13.4.2. Evaluation
13.4.3. Baseline models
13.5. Experiment result
13.5.1. Overall performance comparison
13.5.2. Sensitivity of GeoTab missing data ratio
13.5.3. Impact of adding historical WU in training
13.5.4. Performance on cases with rapid air temperature change
13.5.5. Comparison with HRRR
13.6. Conclusions
References
Chapter 14: Satellite and aerial remote sensing in disaster management: An introduction
14.1. Introduction
14.2. Data and methods
14.2.1. Data
14.2.2. Methods
14.2.2.1. Preparation of spatial-factor layers
14.2.2.2. Architecture of agent-based disaster risk dynamics model (AB-DRDM)
14.3. Results
14.4. Conclusions
References
Chapter 15: Emerging role of unmanned aerial vehicles (UAVs) for disaster management applications
15.1. Introduction
15.2. Disaster management cycle
15.3. Unmanned aerial vehicles (UAVs)
15.4. Overview of UAV sensors
15.5. UAV regulations
15.6. UAV hardware considerations
15.7. Applications of UAVs in disaster management
15.7.1. Land use classification
15.7.2. Early warning systems
15.7.3. Emergency communication networks
15.7.4. Logistics
15.7.5. Baseline data collection
15.7.6. Disaster surveying
15.7.7. Search and rescue
15.7.8. Structural health monitoring
15.7.9. Reconstruction monitoring
15.8. Future applications and challenges
References
Chapter 16: Smart remote sensing network for early warning of disaster risks
16.1. Introduction
16.2. Remote sensing network architecture
16.2.1. Wireless sensor networks
16.2.2. Cloud integration
16.3. Utilizing machine learning for smart sensing
16.3.1. Smart sensing for agriculture applications
16.3.2. Smart sensing for industry 4.0 applications
16.4. UAV potential in early warning systems
16.4.1. UAV for early nature disaster detection
16.4.2. Wildfire detection
16.4.3. Mountain hazards detection
16.4.4. Flood detection
16.4.5. General natural disaster
16.4.6. Cooperative UAVs
16.5. Conclusion
References
Index
Back Cover