Measuring the Forces Inside an Avalanche

Crossbow's innovative inertial systems were recently featured on the TV show “Disaster Lab: Buried Alive” presented by National Geographic.

In a recent scientific investigation conducted on the mountains of Utah, researchers used a Crossbow solid-state Inertial Measurement Unit to capture the rate and acceleration data required to characterize the dynamic forces inside an avalanche. They believe that this data will help in developing a model to better understand the injuries that might be sustained by a human victim, and to help ski patrols find a victim more quickly.

 

Crossbow’s Inertial Measurement Unit (IMU) was installed inside “Ironman”; an anthropomorphic dummy used to represent a human caught up inside an avalanche. Iron Man was able to measure what happens to a human body trapped and buffeted about in an avalanche and provide rescue teams with data they believe will help save lives.

Avalanches are a very serious threat to off-terrain skiers, snowmobilers and climbers. Even small avalanches are a serious danger to life, between 55 and 65 percent of victims buried in the open are killed, and only 80 percent of the victims remaining on the surface survive. Although ski resort safety officers analyze the snow profile daily, and carry out controlled explosions to loosen the ice and snow when necessary, more than one hundred people die in avalanches each year. Snow flow velocities can often exceed 250 kph, creating forces that are sufficient to crush entire buildings.

To view highlights of this investigation watch the segment here.
For more information on MEMSIC's inertial systems visit the site here.

Trillions

 
This video was brought to our attention by our friends at the WSN Blog. It looks ahead to the future of computer sensing networks and was created by Maya, who worked closely with Crossbow on the eKo system.

As we continue to scale the mountain, we are constantly forging ahead looking for ways to connect the physical world with the digital world through wireless sensor networks.

InTech - A Real Mesh

This month InTech Magazine had a special section on automation with wireless technology. The article featured work done by researchers from EPRI, Oak Ridge National Laboratory, Southern Company Services, University of North Carolina at Charlotte, and Taft Engineering who set out to demonstrate the effectiveness of wireless sensors in a power plant. Since low-cost, battery-powered, wireless sensors have the potential to radically alter traditional methods of equipment monitoring in power plants, they wanted to provide guidance for someone deploying a wireless sensor network in a plant on the allowable distance between sensors and what to expect for battery life. The results were a 15-minute powerup of 12 Mote platforms communicating with the base station and no obvious problems.

The research was done using various Crossbow wireless sensor networking prducts such as the MICAz Mote, MDA and MTS sensor boards and the Stargate NetBridge gateway.

Historically, the normal practice in power plants and other process industry facilities has been to hardwire important sensors from their location in the plant to a central equipment room where monitoring systems are installed to collect the data from the sensors. This arrangement worked well, but the cost of installing all the wiring to connect each sensor to the central location was high, often more than the cost of the sensor itself. This high cost has discouraged companies from installing all the sensors they need to fully monitor their plants.

A research project co-sponsored by Southern Company and EPRI to demonstrate wireless sensors in a power plant environment focused on sensors’ radio performance and battery life. Each wireless device, or mote, consists of two small circuit boards, one for the processor and radio and the other for sensors or data acquisition. The research team deployed two types of processors and radios and three types of sensor boards. The sensor boards had several built-in sensors for temperature, ambient light intensity, barometric pressure, sound, magnetic field, and acceleration in x and y directions, along with general purpose data acquisition capabilities. The wireless communication conforms to the IEEE 802.15.4 standard for low power, low data rate sensors with mesh networking capabilities. The team gathered results of a radio frequency site survey conducted before the installation of the sensors.

Wireless sensors
Early wireless sensors used proprietary protocols over a point-to-point radio link and required power high enough to make operation on batteries impractical. In the last few years, a new class of wireless sensors features a standard physical communication layer, a self-forming mesh network, and power use low enough to enable battery life of a year or more. These new wireless sensors have the potential in the next few years to dramatically change not only the wireless sensor landscape but the process monitoring landscape.

Before the power industry can accept any new technology, tests must occur in a realistic power plant environment. Southern Company and the UNC Charlotte conducted two sets of tests focused on wireless communication and battery life. Before testing the sensors in the plant environment, the research team tested them in an office environment to gain understanding of the operation of the sensors and the mesh network.

Plant radio frequency site survey
Before installing any new wireless network technology such as a wireless sensor network, quantify the existing radio frequency (RF) environment to determine if potential interference exists. It is also useful to test the signal propagation characteristics of the facility where you will install the wireless equipment to ensure reliable communication. The U.S. Department of Energy Extreme Measurement Communication Center at Oak Ridge National Laboratory will perform both tests at the E.C. Gaston Electric Generating Plant. Plant Gaston currently does not have any wireless sensor networks installed.

The team used two instruments to record and analyze ambient RF signals. An Aeroflex CS65040 Broadband Signal Recorder and Generator recorded the signals in the time domain, and a Rohde & Schwarz FSH3 spectrum analyzer provided spectral information about the signals. Two special antennas provided the inputs to the analyzers. The monitoring equipment was mounted on a wheeled cart and moved to four sites in the plant. The test site locations were 1) on the turbine deck near the unit 4 turbine, 2) on the mezzanine level under the unit 5 high pressure turbine, 3) on the mezzanine level under the unit 5 generator, and 4) on the base level near a boiler feed-pump turbine.

Data from these tests is presented as screen shots from the instruments. Only a few screen shots are included in the report as examples of the information obtained from the tests.

The signal recorder has an anomaly that appears as four continuous wave (CW) signals, first at -15 MHz from center, second at -10 MHz from center, third at +5 MHz from center, and fourth at +30 MHz. The signal at -15 MHz pulses, while the others are constant. They are present at these frequency offsets in all plots and in all frequency bands. Ignore these signals.

The team set up the signal recorder to collect data in four frequency bands: 500 MHz, 900 MHz, 2.4 GHz, and 5.8 GHz. The bandwidth for all bands is 130 MHz. The plots are the result of replaying the signals recorded during the tests. The average noise floor across the different locations ranged from -79 dBm to -85 dBm in the different frequency bands.

Southern Company mote testing
The project plan for mote testing was to do the initial familiarization and setup and a short test in an office environment and then deploy the sensors in the plant for longer term testing. Testing the wireless sensors in an office environment before testing them in the plant was attractive because the office environment was more controlled, more convenient, and less costly. The objectives of all the tests were:
• Familiarization with the hardware and software being tested.
• Document the range of the low power radio system.
• Verify the operation of the mesh network and its adaptability to network changes.
• Measure the battery usage and estimate battery life.
• Develop a method for measuring temperature with an external thermocouple.

The team set up the initial wireless sensors in an office where the radio environment is friendlier than in a plant. It developed the thermocouple input system using the low level analog input data acquisition board in the office. The monitoring program monitored the sensors and saved the collected data. The team estimated battery life at about 100 days based on one week of operation. The team learned during the office testing that the monitoring program could only collect data from a single type of sensor board at one time. Since two types of sensor boards were seeing use in the project, another program was needed. The mote vendor provided a new gateway product that supported many types of sensor boards at the same time.

The gateway is a small computer running the Linux operating system, which interfaces with the base station on one side and an Ethernet network on the other. The focus of the plant environment testing was to determine how well the sensors communicated in the relatively unfriendly radio environment of a power plant. The goal was to provide guidance for someone deploying a wireless sensor network in a plant on the allowable distance between sensors and what to expect for battery life.

The wall of the control room in which the base station was located is made of sheet metal. To ensure good communication through the wall, the team positioned one mote just outside the control room. The bulk of the remaining motes were at least 100 feet away under the unit 5 turbine.

The computer communicates with the gateway in one of three ways over an Ethernet network. The gateway has a web server built-in, so a web browser in the computer can login to the gateway and view sensor and network data. You can also make a connection using a secure shell (ssh) program on the computer. Finally, you can attach the disk drive on the gateway as a network drive (using Samba on the gateway) to the computer for sharing data files.

The research team distributed 12 motes around the area under the unit 5 turbine. This area has many large steam pipes and structural steel elements that have the potential to obstruct the radio signals from the wireless sensors. In most cases, there was no clear line of sight between motes. Because the team only planned the test to last a couple of months, it placed the motes in plastic zip-lock bags to protect them from dust and water.

The research team powered up the wireless sensor network equipment, and within 15 minutes, most motes were communicating with the base station. A mesh network was established, and all but two motes that could not communicate with the base station directly were able to communicate through the mesh. After the two motes were relocated slightly, they began communicating. The team left the motes in these locations for several weeks, recording sensor data and mesh network performance data continuously. There were no obvious problems with the mote communication, but the mesh structure did change occasionally for unknown reasons.

Visit www.xbow.com for more information on Crossbow's wide range of wireless sensor network platforms and to view the original article visit the InTech site here.

Structural Health Monitoring

Part 2 – Software

 
The Illinois Structural Health Monitoring Project (ISHMP) introduced earlier is working to develop an inexpensive means for continuous and reliable SHM (Structural Health Monitoring) using dense arrays of wireless smart sensors. Researchers have designed, developed, and tested sensors to produce the high-fidelity data required for SHM that can be manufactured very cheaply. Their research has also produced an enabling customizable software framework that greatly reduces both the complexity and time for development of SHM applications for smart sensor platforms.

The ISHMP Services Toolsuite provides an open-source software library of customizable services and examples of SHM applications utilizing wireless smart sensor networks (WSSNs). The ISHMP provides users with a collection of guides to help those who are new to Crossbow’s Imote2 platform, its operating system or programming languages.

As with many wireless sensor platforms, the ISHMP Services Toolsuite on the Imote2 employs TinyOS as the operating system. TinyOS is tailored to the specific constraints of sensor network applications and occupies a small memory footprint, while efficiently supporting complex programs. TinyOS applications are implemented in NesC, a C-like programming language which supports the concurrency model of TinyOS. While TinyOS has been adopted by many sensor network applications and has quite a large user-community, it can be a daunting undertaking for engineers lacking such specific programming experience to develop code for their applications. To alleviate this problem, the ISHMP Services Toolsuite was designed assuming that the typical user will have limited background in computer science. Rather, the user is expected to be familiar with basic Windows operation and have a desire for a smart SHM system that can be installed easily and operated reliably.

 
The ISHMP Services Toolsuite employs a Service-Oriented Architecture (SOA) that lends itself to further expansion, customization, and development of WSSN applications for SHM. It provides complete applications that facilitate common tasks throughout the design, testing, deployment, and monitoring of the SHM system, while utilities offer a set of basic testing and debugging commands to be included with existing applications. The SHM Services Toolsuite includes utilities for resetting nodes remotely, listing the nodes within communication range of the local node, testing radio communication performance, and changing the radio channel and power for local and remote nodes. Specific services and tools currently available include the following:

Foundation Services: The foundation services provide commonly used wireless sensor functionalities that are required to support higher-level applications. These services include basic communication and sensing functionalities.

• Unified Sensing is a TinyOS-based sensing interface for Imote2 that supports various sensor boards including Crossbow ITS400 and Illinois SHM-A. Unified Sensing service provides precise, variable-resolution time stamping of sensor data that can be used to synchronize sensor data from different sensor nodes.
• Time Synchronization is a network-wide service for synchronizing the local clocks that sensor nodes in the network locally have.
• Reliable Communication ensures reliable data communication in a wireless sensor network. Data loss, a common problem in the wireless communication, is handled by the Reliable Communication service.
• Remote Command provides an efficient means for nodes to interact with each other. A command message is delivered to receiver nodes that process the designated tasks, and returns the list of responsive nodes and requested data to the sensor node. Remote Command allows the fault tolerant features to be easily implemented in sensor applications.
• Reliable Multi-Hop Communication provides the beta-implementation of any-to-any reliable multi-hop routing for sensor network applications. Multi-hop communication is essential for monitoring of large structures, where all sensors cannot be deployed within direct communication range of a small number of base stations.

Application Services: The application services provide the numerical algorithms necessary to implement SHM applications on the Imote2s and may also be used independently.

• Synchronized Sensing resamples the sensor data, providing synchronized data. Although the local clocks are synchronized by the Time Synchronization service, the sensing start time and the sampling rate vary from sensor to sensor with a certain amount of error. The unsynchronized sensor data is synchronized by retroactive resampling using the SyncSensing service.
• CFE estimates correlation functions between two arrays of synchronized data.
• CPSD estimates cross power spectral density between two array of synchronized data.
• RD performs the Random Decrement method to estimate unscaled impulse response functions.
• ERA estimates modal properties (i.e., natural frequency, damping factor, and mode shapes) using the Eigensystem Realization Algorithm (ERA). NExT allows the correlation functions estimated from the CFE service to be used in the ERA service.
• SSI performs the covariance-driven Stochastic Subspace Identification (SSI) for modal property estimation.
• FDD performs the Frequency Domain Decomposition (FDD) algorithm for modal property estimation.
• SDLV performs the Stochastic Damage Locating Vector (SDLV) method for damage localization.
• SDDLV performs the Stochastic Dynamic Damage Locating Vector (SDDLV) method for damage localization.

Tools and Utilities: The tools and utilities provide network testing and debugging capabilities that are necessary in any large-scale or long-term WSSN deployment. These tools facilitate evaluation of the network conditions at the structure to determine appropriate values of adjustable system parameters, and assess power consumption and longevity issues.

• LocalSensing collects sensor data from the single Imote2 connected to the PC. This tool is useful for testing new sensor board hardware as well as driver software.
• imote2comm is a terminal program that provides an interface between the PC and the Imote2 through the Crossbow IIB2400 interface board. This tool is frequently used to run the application in the ISHMP Toolsuite.
• TestServices combines numerical services, CFE, ERA, and SDLV, and performs damage detection from user-defined acceleration signals. TestServices is an application example that illustrates how the numerical services can be combined.
• TestRadio allows testing the raw bidirectional communication between a local node and remote nodes.
• RemoteSensing is an implementation of the centralized data acquisition approach that collects synchronized sensor data from a network of sensors to the base station. Time Synchronization and Synchronized Sensing services used in RemoteSensing for the synchronized sensor data.
• SensingUnit, a service component that performs synchronized or unsynchronized sensing, is particularly useful for SHM application development
• IndependentProcessingPSD is an implementation of the independent processing that decentrally estimates the power spectral densities in each node.
• DecentralizedDataAggregation is an application for the data acquisition and processing on decentralized hierarchical sensor network.

Continuous and Autonomous Monitoring Services: These services provide for continuous and autonomous WSSN operation while maintaining power efficiency.

• SnoozeAlarm provides sleep cycle functionality, which greatly reduces long-term power consumption. Sensors sleep for a period of time and then wake up for a relatively short period, during which they can interact with the network. The duty cycle is configurable by the user.
• ThresholdSentry allows a subset of the network to act as “sentry” nodes that are awakened periodically to sense data for a short period of time, determine if an interesting event is in progress, and notify the base station.
• AutoMonitor is a high-level network management application that coordinates each of its components in response to various events. It schedules sensing, data transfer, and ThresholdSentry operation according to a user-specified policy, allowing the network to operate unattended.

In addition, a library of supporting numerical functions that are common to many SHM algorithms is provided including Fast Fourier transform (FFT), singular value decomposition, eigenvalue analysis, etc. Documentation is provided for each service and test application, giving specific requirements and formats of the inputs and outputs for the service. More detailed information regarding the service-oriented architecture of the ISHMP Services Toolsuite can be found here.

To illustrate how SOA of the ISHMP Services Toolsuite lends itself to further expansion and in the development of WSSN applications for SHM, consider the figure below, which shows how the system identification method can be swapped out in an SHM application. In keeping with the SOA framework, these interchangeable services share the same input and output parameters.

 
In its current form, the ISHMP Services Toolsuite requires application programmers to simply provide the code necessary to interconnect the services and tools in a way that makes sense for their applications. In general terms, this code serves the following functions:
        • Run service X at node A
        • Send a control message to node B to run service Y
        • Send results from B to A
        • Run service Z on node A taking as inputs the outputs of services X and Y

Full-scale Integration
As described in the initial post, the software and hardware developed in this research has enabled the deployment of the smart SHM system on the Jindo Bridge in South Korea. This effort is part of a trilateral collaboration between the USA (University of Illinois at Urbana-Champaign), South Korea (Korean Advanced Institute of Science and Technology, KAIST), and Japan (University of Tokyo).

In total, 70 Imote2 sensor nodes with SHM-A sensor boards have been installed on Jindo Bridge. This SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date and demonstrates the suitability of the Imote2 smart sensor platform, the SHM-A sensor board, and the ISHMP software for full-scale, continuous, autonomous structural health monitoring. The figure shown below shows the measured data from WSSN on the Jindo Bridge.

 
The figure below compares computational model predictions (inset) with the experimentally determined frequencies and mode shapes obtained using the frequency domain decomposition method. As can be seen, the agreement is excellent.
 

In summary, the ISHMP Services Toolsuite provides an extensive set of middleware services, utilities, and tools that enable creation of complex, data-intensive WSSN applications for structural health monitoring. This SOA-based approach creates a framework which allows application programmers to more easily create applications for SHM systems. As a result, the framework allows more researchers, and ultimately application engineers, to design and implement successful SHM systems without the requirement of understanding how the underlying middleware and numerical services are implemented.

For information on installing the Toolsuite, visit the ISHMP site here.
For more details on the Imote2 platform, visit MEMSIC site here.

The Illinois SHM Project Services Toolsuite software is open source, but not public domain. The license, a copy of which is also included in the software distribution, allows for unlimited use and modification of the software for non-commercial purposes. Unless otherwise stated, the software and source code in the package is licensed under the OSL Software License (see LICENSE.txt), and is free for non-commercial distribution. Also included are modified files from TinyOS (BSD and Intel Open Source licenses) and LAPACK 3.0 (no formal license, free for redistribution); these licenses are indicated at the top of the respective files.