Improving geological disaster monitoring capabilities using wireless sensor networks

Technical Overview

Wireless sensor networks were born in the late 1990s. They were originally proposed by the US military as a new technology for battlefield environment monitoring. A large number of cheap sensor nodes were spread across a designated area, and the data was transmitted back to the monitoring center through the wireless network. All information in the monitoring area would be visible to the observer.

The project is led by the University of California, Berkeley, and after the initial products are completed, the American Crossbow Technology Company will be responsible for civilian promotion.

Compared with traditional networks, the most obvious feature of wireless sensor networks can be summarized in six words, namely "self-organization and self-healing". Self-organization means that in wireless sensor networks, unlike traditional networks, which require artificial topology, each node can automatically detect neighboring nodes after deployment and form a mesh of multi-hop routes that eventually converge to the gateway node. The whole process does not require human intervention. At the same time, the entire network has dynamic flexibility. When any node is damaged or a new node is added, the network can automatically adjust the route to adapt to changes in the physical network at any time. This is the so-called self-healing feature.

These characteristics enable wireless sensor networks to adapt to complex and changing environments and monitor harsh environments that are difficult for humans to reach. After the Wenchuan earthquake, all communication facilities were interrupted. In the later period, we could only rely on manpower to detect aftershocks, landslides, and barrier lakes. This was inefficient and lacked quantitative data for scientific analysis and prediction. This problem can be effectively solved if wireless sensor networks are deployed in the disaster area. Most wireless sensor network nodes are small in size. Crossbow's Mica series nodes are only the size of two No. 5 batteries. Battery power can guarantee several months of working time, and there is no need to pull wires on site for power supply. It is very convenient to flexibly deploy monitoring and predict the occurrence of geological disasters in emergency situations.

1. Landslide

Hong Kong has a large number of mountainous landforms and a large urban population, which requires a high land utilization rate. Therefore, a large number of buildings and roads are located near the mountains. Due to its location in southern China, the region has high rainfall all year round, especially in the rainy season in summer. Unstable mountainous landforms are prone to landslides after being eroded by rain, posing a huge threat to the safety of life and property of residents.

In the past few decades, many landslides have occurred in some extremely dangerous areas, so the government has tried to deploy a flexible and stable system to monitor and warn of landslides. The government has tried to deploy multiple wired monitoring networks, but since the monitoring areas are often in remote mountainous areas, there are no roads, field wiring, and power supply are limited, making it very difficult to deploy wired systems. In addition, the wired method often uses the method of deploying Dataloggers nearby to record and collect data, and special personnel are required to go to the monitoring points regularly to download data. The system cannot obtain real-time data and has poor flexibility.

After many exchanges with geographical monitoring experts and several field visits, Crossbow helped geological professional companies FT and Fugro deploy a landslide monitoring solution based on wireless sensor networks in the Qingshan and Lantau areas of Hong Kong. The monitoring of landslides mainly relies on the role of two types of sensors, liquid level sensors and tilt sensors. In areas where the mountain is prone to danger, multiple holes will be set vertically along the mountain, as shown in the figure.

Each hole will have a level sensor deployed at the bottom and several tilt sensors deployed at different depths. Since landslides in the area are mainly caused by rainwater erosion, the depth of the groundwater level is the first indicator of the risk of landslides. The data is collected by the level depth sensor deployed at the bottom of the hole and sent by the wireless network.

We can monitor the movement of mountains through inclination sensors. Mountains are often composed of multiple layers of soil or rock. Different layers move at different speeds due to different physical compositions and erosion levels. When this happens, the inclination sensors we deploy at different depths will return different inclination data. After the wireless network obtains the data from each inclination sensor, through data fusion processing, professionals can determine the trend and intensity of the landslide and determine its threat level.

Landslides can be seen everywhere in the disaster area after the earthquake, especially on both sides of the main traffic routes, which pose a huge threat to the rescue progress. I believe that countless people still remember the heart-wrenching feeling when they heard that the lifeline from Li County to Wenchuan was interrupted by landslides less than a day after it was opened.

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2. Earthquake monitoring

Earthquakes are caused by the release of energy from changes in the earth's crust, which forms mechanical waves that are transmitted on the surface. Therefore, vibration sensors placed on the surface can be used to detect the occurrence and intensity of earthquakes. The magnitude of the Wenchuan earthquake in Sichuan was 8, and the subsequent aftershocks were obtained by the earthquake bureau gathering information from vibration sensors deployed in various places, and then restoring it to the vibration data of the earthquake center.

Of course, since the deployment location of the earthquake monitoring network is fixed, wired monitoring is a more appropriate choice. However, in emergency situations, a wireless earthquake monitoring network that can be deployed at any time to obtain data is also of considerable significance. For example, after an earthquake, it can be used to monitor the occurrence of aftershocks. The transmission of mechanical waves is much slower than that of radio waves, so it can save a few minutes of precious warning time for rescue workers to retreat.

Harvard University in the United States deployed a similar emergency earthquake monitoring system last year, which is mainly deployed in volcanic areas to monitor earthquake information caused by volcanic eruptions. The system uses TelosB wireless sensor nodes equipped with 24-bit ADC to monitor weak vibration information transmitted by MEMS accelerometers. The nodes are deployed radially with the crater as the center, and one node is deployed every hundreds of meters. After deployment, the vibration information of each point along the radial propagation of the earthquake can be monitored.

Similar systems will be of great significance in aftershock monitoring and post-earthquake emergency deployment. The China Earthquake Administration, Harbin Institute of Engineering Mechanics, and Taiwan Earthquake Research Center have all begun to conduct research on similar projects in recent years. We look forward to the emergence of similar equipment in the near future.

3. Building health monitoring

In an earthquake, the biggest damage to people's lives and property is the collapse of buildings. In today's metropolises, skyscrapers are everywhere. In the Wenchuan earthquake, the Beijing area also felt the tremors, and high-rise office buildings were shaking, causing many people to feel uncomfortable. However, the office staff did not start to evacuate until the earthquake was confirmed through broadcasting and the Internet. If the epicenter was near Beijing, these few minutes of hesitation would result in the loss of thousands of lives in high-rise office buildings, and Beijing has at least hundreds of high-rise office buildings.

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Accelerometers are still the simplest and most effective way to monitor buildings. The University of California, Berkeley, with the assistance of Cross-bow, has deployed a building health monitoring system on the Golden Gate Bridge in San Francisco. Its original intention was to detect the vibration of each key stress point of the bridge under the action of wind. After the overall data modeling, it can be analyzed to find the parts of the bridge that are seriously damaged and aged, so as to carry out targeted repairs.

Bridges and high-rise buildings have a common feature, that is, the building structure is extremely sensitive. Therefore, it is difficult to deploy the measurement points at the front end in a wired manner, otherwise it is very easy to damage the building structure. Wireless technology, especially low-power wireless technology that does not require power supply, is extremely important in solving the problem of obtaining 100-meter data at the front end of building health monitoring. The node has wireless capabilities and is relatively small in size. It can be easily installed at the key stress points of the building without affecting the appearance of the building. With low power consumption, the node does not need to be replaced frequently once deployed. The complex and time-consuming wiring operation is eliminated. As long as the node switch is turned on, the receiving terminal located in the building monitoring center can obtain data in real time. After linkage with the building alarm system, once the vibration information that may threaten the building is detected, an alarm will be immediately issued to notify the people in the building to evacuate. The data collected by the system can also be used to monitor the aging of the building and provide auxiliary decision-making information for building maintenance.

Crossbow has established a joint laboratory in China with a research team led by Academician Ou Jinping of Harbin Engineering University, which specializes in research on building health monitoring. The relevant research results have been applied in the maintenance of several bridges in China. Crossbow has also launched an entry-level vibration kit with a full set of source code, which supports six nodes to synchronously collect 4G accelerometer information and transmit it back to the gateway, and conduct post-modeling analysis through Labview, which can serve as a reference design for researchers and engineers.

Problems and Solutions

1. Communication distance

The biggest problem when applying wireless sensor networks in the wild is how to ensure that the Mote nodes can still communicate normally under heavy vegetation cover. Mountainous areas prone to geological disasters are often densely vegetated. Before carrying out the Hong Kong project (the environment is very similar to mountainous areas, with few people, wild grass up to one person tall and a large number of trees), Crossbow sent people to conduct several field investigations and conducted detailed discussions and analysis. In the end, 2.4GHz was considered to be the most suitable for use in this environment.

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As can be seen from the table above, both heavy vegetation and heavy rain will attenuate wireless signals. 433MHz has better diffraction performance due to its longer wavelength, and performs better in rain. 2.4GHz has better penetration due to its shorter wavelength, and performs better in heavy vegetation environments. According to the table above, the attenuation caused by heavy vegetation is thousands of times that of heavy rain, and the system should work in a rainy environment for less than 50%. Therefore, 2.4GHz should be more suitable for use in outdoor environments.

In addition, considering the spectrum environment, commercial devices currently using 2.4GHz, such as WiFi and BlueTooth, are mostly short-range devices, so the 2.4GHz band is relatively clean and has less interference. The interference of 400MHz and 900MHz is relatively more. When geological disasters occur, a large number of individual radios and walkie-talkies are very likely to cause mutual interference. From the perspective of avoiding interference, 2.4G is a better choice.

Although 2.4GHz has a relatively good performance, heavy vegetation and rainfall still cause significant attenuation of wireless signals. Crossbow launched the IRIS node in 2007. Due to the use of the new AT1281 + RF230 chipset and modular design and production, IRIS has greatly improved the communication distance index, while its power consumption has been reduced to a certain extent.

As shown in the figure above, the node achieved a communication distance of one kilometer in the lake environment test in Beijing's Houhai area. After replacing the 5dBi gain antenna, the IRIS node also achieved a communication distance of 500 meters in the dense traffic during rush hour on Beijing's Second Ring Road. Its power consumption was reduced by about 1/3 compared to the original MicaZ node.

2. Energy consumption

Each node is powered by a battery. Under Crossbow's ELP power management mechanism, the battery power can keep the node working continuously for more than 4 years. ELP is the Extend Low Power mode, which is an improved version of Crossbow's original Low Power mode and can provide better power performance. The weather detection wireless sensor network for Antarctic scientific research, jointly developed by Crossbow and the Institute of Remote Sensing, Chinese Academy of Sciences, can maintain a working time of up to one year at minus 80 degrees.

The battery voltage is monitored at all times. Once the voltage is too low, the node will send the voltage data to the base station. After the data is successfully sent, the node will be in deep sleep mode. After the administrator receives a warning that a node voltage is too low, he can carry out system maintenance work purposefully. When the node is replaced with a new battery, it will automatically work normally.

3. Reliable communication

Wireless communication has a certain data loss rate. When used in environmental monitoring, the loss of collected information once will not have any impact on the global massive data. However, when used in address disaster monitoring, the information it transmits is of great importance, and once lost, the impact is extremely serious. End-to-End ACK provides end-to-end information transmission confirmation, which is specifically used to send similar concerned data packets. In this mode, after each data packet reaches the destination node after multi-hop transmission, the destination node will immediately send back an ACK data packet. If the sender does not receive the ACK data packet after a certain time delay (determined by the number of hops in the routing table), it will immediately resend it and repeat the process until the data packet safely reaches the destination.

Summarize

As a relatively new technology, wireless sensor networks can solve many problems in daily life with their unique capabilities. This article combines the author's professional experience to provide a concept of using new technologies to improve monitoring capabilities for geological disaster monitoring similar to the Wenchuan earthquake. However, there is still a long way to go from conception to realization, which requires the unremitting efforts and investment of scientific and technological workers. Compared with the huge number of software and embedded developers in China, there are still only a handful of engineers in China who are familiar with and understand the development of wireless sensor networks. From a software perspective, when writing programs for wireless sensor networks, the entire network must be considered, similar to extending the concept of threads to hundreds or thousands of CPUs, cooperating with each other to complete specific tasks. From a hardware perspective, when designing wireless sensor network nodes, the low-power design of external circuits must be considered. Compared with the working time of mobile phones and MP3s, which can be one to several days, wireless sensor networks must consider the working time of limited batteries for months or even years.