Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey
Abstract
:1. Introduction
- Several types of seismic waves radiate from an earthquake’s epicenter. Sensors are activated by P-waves, which are weaker but move more quickly. Thereafter, sensors send signals to cloud servers for processing.
- Algorithms in the cloud server instantly determine the location, magnitude, and severity of an earthquake. How big is it? Who will suffer from this?
- The technology sends out an alert before slower but more destructive S-waves and surface waves arrive.
- We clarify why the EEWS is advantageous for smart cities.
- We emphasize the growth of IoT usage, as well as the IoT system framework in general and its constituent parts.
- We have developed a thorough taxonomy of IoT devices that includes various topics such as the source of data, environment, measured parameters, and factors of validation.
- We present a standard design for the IoT that takes into account potential emergency management.
- We discuss the verification and validation concerns related to using IoT-based EEWS.
2. Seismic Waves and Seismic Signal Processing Techniques
- Primary waves, also referred to as P-waves, are longitudinal compressional waves that move through the earth in a straight line. These waves are known as “primary” waves because they arrive first at seismograph stations, traveling faster through the earth than other types of waves. P-waves are pressure waves that can travel through any material, including fluids, and move at a speed that is around 1.7 times faster than that of S-waves. In contrast to S-waves, which are transverse waves that move side-to-side, P-waves are compression waves that cause particles in the material they are traveling through to move back and forth in the direction of the wave’s propagation. They take the form of sound waves in the air and move at the same velocity as sound waves, which is around 330 m per second on average. The ability of P-waves to travel through any material allows them to be used to study the interior of the earth. By measuring the time taken for P-waves to travel through the earth from an earthquake’s epicenter to a seismograph station, scientists can calculate information about the earth’s internal structure. For example, the average speed of P-waves in granite is roughly 5000 m per second, while in water, it is around 1450 m per second. This information can be used to create a detailed model of the Earth’s interior.
- S-waves, also known as secondary shear waves, are transverse waves that cause the ground to shift in a direction perpendicular to their propagation during an earthquake. These waves arrive at seismograph stations after P-waves, which are faster. S-waves have a horizontal polarization and move in a horizontal direction, causing the ground to shift from side to side. However, S-waves can only travel through solids since liquids and gases do not support shear forces. They move through any solid medium at a speed that is approximately 60% slower than P-waves. The absence of S-waves in the outer core of the Earth is consistent with the presence of liquid. This is because S-waves cannot propagate through liquids, and their absence indicates that the outer core is predominantly liquid. However, P-waves can propagate through liquids, which is why they can travel through the entire Earth. The study of seismic waves and their behavior has provided scientists with valuable insights into the structure and composition of the Earth’s interior.
3. IoT-Cloud Systems
3.1. IoT Systems
- Providing the node with an interface that can collect data from the environment.
- Providing a tool for acquiring and analyzing data in order to derive knowledge from it.
- Taking action and communicating choices and information to the appropriate hubs.
3.2. Cloud and Fog Systems
4. IoT-Cloud-Based EEWS
- Simulation testing involves creating a virtual environment that simulates real-world conditions, including seismic activity and sensor data [200,201]. Simulation testing allows researchers to test the performance of an EEWS system under different scenarios, such as different magnitudes and distances of earthquakes and different types of seismic waves [202]. This technique can also be used to evaluate the effectiveness of different algorithms and parameters used in the system [203].
- Field testing involves deploying an EEWS system in real-world conditions and collecting data on its performance and reliability [204,205]. Field testing can provide valuable insights into the system’s performance under actual operating conditions, which may differ from those in a simulated environment. Field testing can also help to identify potential issues with the system, such as sensor malfunction or communication failures [206]. This technique can be time-consuming and resource-intensive, but it provides valuable data on the system’s performance and reliability in real-world scenarios [207].
- Data-driven analysis involves analyzing large datasets generated by an EEWS system to identify patterns and trends, which can provide insights into its performance and reliability [208]. Data-driven analysis can be used to identify correlations between sensor data and earthquake characteristics, such as magnitude, duration, and intensity [209]. This technique can also be used to identify anomalies in sensor data, which may indicate issues with the system’s performance or reliability [210]. Data-driven analysis can provide valuable insights into the performance and reliability of an EEWS system over long periods of time [211].
5. Validation and Verification Aspects
5.1. Different Categories of V&V Techniques
5.2. Adaptation of V&V Techniques for EEWS
5.3. Cost and Limitations of V&V Techniques
6. Open Challenges, Conclusions and Future Directions
- Sensor network reliability and accuracy: One of the primary challenges in implementing IoT and cloud-based EEWS is ensuring the reliability and accuracy of the sensor network. These systems rely on a network of sensors to detect and measure seismic activity, making it essential to ensure that the sensors are functioning correctly.
- Real-time data processing and decision-making: EEWS require fast and accurate data processing and decision-making capabilities to provide timely alerts to people and organizations in affected areas. This requires sophisticated algorithms and real-time data processing capabilities, which can be challenging to implement in IoT and cloud-based systems.
- Secure communication channels: The transmission of data between sensors, cloud facilities, and other components in an EEWS must be secure to prevent unauthorized access and tampering. Ensuring the security of communication channels is a significant challenge in designing and implementing these systems.
- Heterogeneity and scalability: IoT and cloud-based systems are inherently heterogeneous, with devices and services from different manufacturers and with different capabilities. Ensuring seamless integration and scalability of these systems is a significant challenge, particularly as the number of devices and sensors in the network increases.
- Cost-effectiveness and sustainability: Implementing an EEWS using IoT and cloud facilities can be costly, requiring significant investment in hardware, software, and personnel. Ensuring the cost-effectiveness and sustainability of these systems is a significant challenge, particularly in regions with limited resources.
- Usability and accessibility: EEWS must be usable and accessible to people and organizations in affected areas, including those with limited literacy or technical skills. Ensuring the usability and accessibility of these systems is a significant challenge, requiring careful consideration of user needs and preferences.
- Privacy and ethical concerns: The collection and processing of data in EEWS raise privacy and ethical concerns, particularly as these systems become more sophisticated and widespread. Ensuring that these systems comply with relevant regulations and ethical principles is a significant challenge.
- Interference from environmental factors: EEWS can be affected by environmental factors such as electromagnetic noise and weather conditions, which can interfere with the accuracy and reliability of the sensor network. Ensuring the robustness and resilience of these systems is a significant challenge, requiring careful consideration of environmental factors.
- Continuous monitoring and maintenance: IoT and cloud-based EEWS require continuous monitoring and maintenance to ensure system performance and reliability. Ensuring the continuous monitoring and maintenance of these systems is a significant challenge, requiring robust and scalable infrastructure and skilled personnel.
- Development of more efficient and accurate sensors: Research and development should focus on developing more efficient and accurate sensors that can accurately detect and measure seismic activity while also being cost-effective and scalable.
- Standardization of communication protocols: The standardization of communication protocols can help to ensure the interoperability and scalability of IoT and cloud-based EEWS. This can simplify the integration of different devices and services, reducing the complexity of these systems.
- Adoption of free, open-source software: The adoption of free, open-source software can help to reduce the cost and complexity of developing EEWS while also encouraging collaboration and innovation in this area.
- Engagement with local communities: Engagement with local communities can help to ensure that EEWS are developed in a form that meets the needs and preferences of people and organizations in affected areas. This can improve the usability and effectiveness of these systems in real-world scenarios.
- Development of new funding models: The development of new funding models, such as public–private partnerships, can help to ensure the sustainability and scalability of EEWS. This can provide the necessary resources and expertise to develop and maintain these systems over the long term.
- The “last kilometer” problem: This problem is the difficulty of assuring prompt and efficient warning, communication, and reaction systems to people and communities in the final seconds before the occurrence of powerful and devastating S-wave shaking during an earthquake. In particular, it requires addressing densely populated areas where the window for preparation and evacuation is constrained, where there is a gap between earthquake EEWS and the capacity to reach and notify individuals in the impacted area. In order to protect people’s safety and well-being in the final crucial seconds before the arrival of the destructive seismic waves, this topic focuses on the necessity for the effective broadcast of alerts and emergency instructions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
EEWS | Earthquake Early Warning Systems |
SDN | Software Defined Network |
AI | Artificial Intelligence |
NFV | Network Functions Virtualization |
DMSEEW | Distributed Multi-Sensor Earthquake Early Warning |
Micro-MEMS | Micro-Electro-Mechanical systems |
ML | Machine Learning |
IoT | Internet of Things |
UG | Underground |
ODLOS | Outdoor Line-of-sight |
UAV | Unmanned Arial Vehicle |
IDLOS | Indoor Line-of-sight |
UW | Under Water |
OD | Outdoor |
ID | Indoor |
DT | Decision Tree |
RF | Random Forest |
SVM | Support Vector Machine |
NB | Naïve Bayes |
KNN | K-Nearest Neighbor |
FD | Federated Learning |
GPS | Global Positioning System |
5G | Fifth Generation |
B5G | Beyond Fifth Generation |
AE | Autoencoder |
CNN | Convolutional Neural Network |
Body waves | P/S-wave |
NIED | National Research Institute of Earth Science and Disaster |
V&V | Verification and Verification |
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Ref. | Utilized Technology | Main Focus | Methodology | Contributions |
---|---|---|---|---|
[86] | Geophysical technology | Earthquake and catastrophe management | Literature review | Earthquake hazard, vulnerability, risk analysis |
[87] | Remote sensing | Earthquake management | Review of remote sensing applications | Remote sensing pros and cons in earthquake research |
[88] | UAV hardware | Disaster relief | Field trials and case studies | Implementable framework for drone data collection and analysis for disaster preparedness, response, and recovery |
[89] | IoT technology | Disaster management | Comparative analysis of IoT-based disaster management options | Practical applications of IoT technology for disaster management |
[90] | Modern technology | Disaster and risk management | Evaluation of available and applied technology | Suggestions for improving technology adoption across all DRM pillars |
[91] | Mapping techniques | Mapping in post-earthquake settings | Evaluation of ML and deep learning frameworks | Identification of research gaps and possibilities for real-world scenarios |
[92] | Remote sensing | Remote sensing data and methods for earthquake risk assessment | Review of remote sensing applications | Necessity for a complete, interdisciplinary approach to earthquake risk assessment |
[93] | Satellite images | EEWS | Literature review | Evaluation of current and potential applications of remote sensing for seismic disaster early warning |
[94] | Remote sensing | Post-earthquake damage assessment | Case studies and literature review | Identification of challenges and opportunities in remote sensing for post-earthquake damage assessment |
[95] | Emerging technologies | Disaster management | Literature review and text mining | Analysis of the effects of emerging technologies on disaster management |
[96] | Digital tools | Managing existing structures in earthquake settings | Case study | Procedure for managing pre- and post-earthquake stages of existing structure management using digital tools |
Our Work | IoT nodes and cloud infrastructure | EEWS, environment type, data type, and source, measurement parameters, cloud infrastructure | Literature review and analysis | Comprehensive overview of the role of IoT and cloud infrastructure in EEWS, including a generic architecture and verification and validation methods |
Advantages | Limitations |
---|---|
Good performance in autonomous processes | Requirement of continuous connectivity with the controllers, network coordination |
Long-distance flights, despite the need for line-of-sight, thus large coverage area | Range limitation proportional to the physical capabilities such as radio controller’s range, line-of-sight, and positioning |
Transmission of big data to the cloud | Limited ability for intelligent data processing |
Fast-deployed, flexible, and on-demand operative structure | Modeling complexity |
Low-cost values | The necessity of Quality of Service optimization |
Usage in dangerous areas | Security challenges such as hijacking |
Ref. | Sensor Node | Employed Environment | Used Data Type | Used Measurement Parameter | Source |
---|---|---|---|---|---|
[184] | Acceleration sensors (MMA8452, LIS3DHH, ADXL355, and MPU9250) | UG | Acceleration data | PGA | NIED and USGS |
[180] | Mobile node | Coastal areas | Tsunamic data | Hypo-center and magnitude | NOAA |
[177] | UAV nodes | ODLOS | Aerial images data | Received frames/sec | Local drones |
[185] | Smartphones | S-D environment | Acceleration data | Earthquake data | NIED and USGS |
[188] | Seismometer | UG | GPS and weak motion data | Earthquake data | IRIS and NIED |
[173] | MEMS | UG | Acceleration data | Acceleration, SNR | NIED |
[174] | Arduino Cortex M4 | UG | Acceleration data | Earthquake detection accuracy and detection latency | Local data observed by MEMS accelerometers |
[175] | Acceleration nodes | IDNLOS | Acceleration data | PGA and human activity | Local distributed smartphones |
[176] | Soil and terrain nodes | UG | Soil moisture, shear strength of the soil, severity of the rain | Soil moisture, Soil shear strength, rain severity | GSI |
[53] | Tmote Sky | ID and OD | Seismic velocity data | Location and magnitude | JMA and Hi-net |
[179,195] | IoT gateway | UG | Seismic waveform | Earthquake predictions | Local datasets and regional data |
[183] | Acceleration nodes | UG | Acceleration data | PGA | NIED |
[185] | MEMS | Noisy environments | Seismic waveform | P-wave arrival | STEAD |
[182] | Raspberry Pi | Mesh network | Seismic waveform | Local earthquake | Locally observed |
[196] | SSN/SOSA ontology | UW | Volcanic data | Volcano-tectonic, long-period earthquakes, underwater explosions, and quarry blasts | Local data |
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Abdalzaher, M.S.; Krichen, M.; Yiltas-Kaplan, D.; Ben Dhaou, I.; Adoni, W.Y.H. Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey. Sustainability 2023, 15, 11713. https://doi.org/10.3390/su151511713
Abdalzaher MS, Krichen M, Yiltas-Kaplan D, Ben Dhaou I, Adoni WYH. Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey. Sustainability. 2023; 15(15):11713. https://doi.org/10.3390/su151511713
Chicago/Turabian StyleAbdalzaher, Mohamed S., Moez Krichen, Derya Yiltas-Kaplan, Imed Ben Dhaou, and Wilfried Yves Hamilton Adoni. 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey" Sustainability 15, no. 15: 11713. https://doi.org/10.3390/su151511713
APA StyleAbdalzaher, M. S., Krichen, M., Yiltas-Kaplan, D., Ben Dhaou, I., & Adoni, W. Y. H. (2023). Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey. Sustainability, 15(15), 11713. https://doi.org/10.3390/su151511713