How IoT Is Revolutionizing Disaster Prevention and Response

Mark Patrick – September 25, 2024

Collected at: https://www.iotforall.com/how-iot-is-revolutionizing-disaster-prevention-and-response

Disasters can quickly impact the well-being of people and the planet. Whether caused by naturally occurring events such as hurricanes or floods or by human activities such as industrial accidents, sudden catastrophic incidents can result in human, environmental, and economic harm.

But forewarned is forearmed. Disaster prevention systems can play a critical role in saving lives and mitigating economic losses. Increasingly, sensor-based IoT technologies can provide vital early warnings of dangerous events or play a key role in managing the aftermath, thereby reducing further harm.

According to the UN Office for Disaster Risk Reduction (UNDRR), disaster prevention and warning systems can shift the focus from managing disasters to managing risk. This helps to break the cycle of catastrophe–response–dependency. The economic benefit of such an approach is enormous. The UNDRR says that investing one dollar in making infrastructure disaster-resilient saves four dollars that would otherwise go toward rebuilding.

From Floods to Oil Spills: Categorizing Disaster Types

So, what types of disasters are most prevalent? And what sorts of impact can they have? They fall into two categories: natural and human-made. Both can have a significant effect, including loss of life, economic damage, and environmental destruction. The most frequently occurring natural disasters include floods, droughts, wildfires, earthquakes (Figure 1), and tsunamis. In 2023, natural disasters caused approximately 95,000 reported fatalities worldwide and resulted in economic costs of around $380 billion.

Meanwhile, human-caused disasters can include industrial accidents, oil spills, building collapses, high-rise fires, transportation accidents, and acts of terrorism. The number of fatalities from human-caused disasters can vary dramatically depending on their nature and scale, although the number can run into the tens of thousands worldwide.

Subsequently, there is much interest in technology and disaster prevention/warning strategies. The combination of sensor-based IoT, wireless technologies, and artificial intelligence-based analytics holds the key to many advances in this area. These technologies can revolutionise disaster prevention by enabling interconnected devices to collect and share data. The insight gathered can then form the foundation for more informed decision-making.

IoT’s Active Role in Disaster Prevention

Let’s look in more detail at how these technologies piece together. Low-powered sensors and devices form the fundamental building blocks of disaster prevention systems, enabling long-term deployment in remote or harsh environments without frequent maintenance. These sensors can detect changes in specific conditions, such as temperature, vibration, and air pressure, providing critical early warning indicators of impending natural disasters such as earthquakes and tsunamis.

They can also measure and monitor water levels in flood-prone areas, allowing local communities to move to more elevated ground when risk is high. Deploying sensors can also help prevent human-caused incidents. For example, smoke detectors can provide fire alerts, and air quality monitors can indicate the release of hazardous substances or high levels of air pollution.

The Role of Advanced Sensors, AI, and Communication Technologies

Technological advances mean sensors are now smaller and more energy-efficient, resulting in their use in more applications. Developments in micro-electromechanical systems (MEMS) technology have resulted in sensor miniaturisation. Meanwhile, new battery architectures deliver improved energy density, allowing them to last longer in the field. Sensors can also integrate solar, thermal, and vibration energy-harvesting systems, enabling devices to operate independently in a wider range of environments.

But sensor-based data collection is just one part of the puzzle. Deploying software-based systems that incorporate AI and machine learning is essential for analyzing data to predict and detect potential disasters before they occur. AI can play a vital role in pattern recognition, predictive analytics, and anomaly detection—often in real time—alerting the relevant authorities and the public as quickly as possible. AI-based computation can occur in the cloud or at the edge, depending on factors like security, latency, and the need for large data storage capacities.

Thirdly, for cloud-based systems, advanced communication technologies like 5G and LoRa have dramatically improved data transmission speeds and reliability in disaster scenarios. These protocols have their own set of performance characteristics: 5G is superior in data rate, range, and latency, while LoRa excels in battery life, power usage, and cost. So, developers of disaster prevention and warning systems have IoT technology options to meet their needs.^®

Highlighting the Benefit of Real-World Projects

In this section, we look at real-world examples of disaster-preventing technology in action. In the US, for example, the ALERTCalifornia public safety program from the University of California San Diego involves the deployment of IoT sensors, cameras, drones, and AI to detect and monitor wildfires (Figure 2) in California, improving early detection and response.

ALERTCalifornia represents the third generation of the High Performance Wireless Research and Education Network (HPWREN), a system that has been evolving for about 20 years in the US. As early as the 2003 Cedar Fire, emergency managers were already employing real-time wildfire images captured by high-resolution motion-detection cameras and sensors that had been installed beforehand, highlighting the usefulness of wireless technologies in effectively managing wildfire scenarios.

The high-definition cameras pan, tilt, zoom, and perform 360-degree sweeps approximately every two minutes with 12 high-definition frames per sweep to provide complete visibility of fire risk. The cameras also offer 24-hour monitoring with near-infrared night vision capabilities. Meanwhile, drones with scanners generate three-dimensional information about scanned surfaces, while lidar remote sensing allows ALERTCalifornia to examine fire-prone environments. AI is used for data fusion and analytics to make sense of the data provided.

Meanwhile, IoT-based infrastructure is being deployed in the UK to provide early warning of flooding threats, ensuring public safety and infrastructure protection. For example, in the Huntingdonshire district of Cambridgeshire, the Flood Sensor pilot project, funded by the Cambridgeshire & Peterborough Combined Authority and Huntingdonshire District Council, uses sensors connected via the LoRa network to collect real-time river flow and water level data. When a flood risk is detected, residents and businesses receive a warning, allowing them to take actions to help keep them safe and limit the damage to houses and other infrastructure.

Recovering from Disaster

ALERTCalifornia and the Flood Sensor project are just a couple of examples of live disaster warning and prevention programmes amongst many other global initiatives. IoT networks have proved beneficial in a number of disasters, not only helping to reduce or avoid damage but also providing key assistance to help recover from disasters.

During hurricanes or floods, the loss of communication due to damaged cellular networks can greatly hinder the process of disaster recovery. In the aftermath of Hurricane Maria in Puerto Rico in 2017, temporary cellular devices were deployed to restore communication networks that had been devastated by the storm. Using solar-powered sensors and satellite-connected communication hubs, temporary communication links were established, enabling emergency response teams to coordinate relief efforts effectively and reach isolated communities in need of assistance.

In a report on the 2017 Atlantic hurricane season, the Global System for Mobile Communications Association (GSMA) stressed the significance of implementing an enhanced early warning system. Furthermore, they pointed out the potential benefits of employing the early warning system to activate collapsible network masts, which would effectively lower the towers ahead of the hurricane’s arrival. This proactive measure would not only prevent damage but also streamline reconnection efforts after the storm dissipates.

As perception, communication, and analytic technologies advance, the value of IoT in assisting the recovery from both human-caused and natural disasters will rise. By implementing low-cost, rugged solutions, agencies can expedite the restoration of critical infrastructure, support vital rescue missions, and collect valuable data to drive the creation of future prevention strategies, such as enhanced flood defences or seismic-resistant architecture.

Conclusion: IoT Can Save Lives and Protect the Environment

Disaster prevention and warning represent a sound example of IoT for good. The combination of rugged and low-power sensors, wireless connectivity, and AI-based data analytics provides authorities and first responders with access to real-time information that wasn’t possible to gather in the past. This information can result in more effective decision-making. As technology advances, these systems will become more pervasive and offer ever more granular insight into the world around us.

IoT-based systems and technologies are here to stay. They will continue to play a critical role in disaster prevention and management, saving lives, mitigating economic losses, and protecting the natural environment.