ASSERT Yourself!

Someone once said, "To know oneself, one should assert onself." Researchers at the University of Texas at Dallas Distributed Systems Lab, took this notion and created an Advanced wireleSS Environment Research Testbed. ASSERT provides users with the true ability to know oneself. In many instances, simulation allows for rapid development and testing, but it does not provide a realistic RF environment. To compensate for this field experiments are performed yet when problems are encountered, it can be difficult to located and correct them. ASSERT provides users with the advantage of simulations and field experiments without the suffering and disadvantages of both! The goal of this testbed is to provide experimenter with the ability to run controlled, repeatable experiments that would have a similar performance to that of field experiments without the associated deployment and travel costs.

ASSERT has been a work in progress for the past two years and currently offers a forty site testbed. As wireless networking is becoming more pervasive there has been a greater desire to develop communication hardware and protocol stacks that have a number of desirable properties like increased throughput, reduced latency, reduced energy consumption, quality of service security, etc. ASSERT is a true emulation testbed. Field testing requires extensive time and energy. Changes to the location of devices require physical movement of the equipment and measurement devices from place to place making repeatability a difficult task. 

The hardware for each site consists of two mated boards, the Site board and the RF board. Each Site board consists of an ARM 200MHz processor, 256MB of on board RAM, a Xilinx FPGA, support for an external clock and various peripheries such as Ethernet and RS-232. The RF board consists of 17 SMA coaxial connectors, 16 of which are used to connect to other Site-RF board pairs and 1 dedicated to the Unit Under Test (UUT). The current system integrates the Crossbow/MEMSIC MICA2 (900MHz) Mote platform with gateways as UUTs, however any device operating in the 700MHz to 1100MHz range is natively supported. 

ASSERT emulates the distance between multiple devices by using variable attenuation - this means that identical tests can be performed in the lab compared to the field. ASSERT also allows users to conduct experiments in licensed frequencies like the cellular service bands without interfering with the services offered by the owners of these licensed bands. By changing signal attenuation levels, using programmable attenuators and amplifiers on the path between nodes, UT researchers emulated the formation/disruption of wireless links and node mobility. Using power dividers/ combiners they are able to emulate complex, multi-hop wireless sensor networks. As all communication is over coaxial cables, the network is immune to interference from other wireless networks, or allows the flexibility for users to inject the desired amount of noise into the links.

The ASSERT software performs a variety of tasks including monitoring for faults, efficiently allocating resources and providing an easy to use interface. ASSERT currently consists of forty nodes and is designed to scale to at least one thousand nodes without any design changes. Researchers at UT Dallas are converting the UI from the current small Java program to a web based application so that the experiment set up and data gathering is done by the experimenter from their browser. Through the sophisticated custom hardware and easy-to-use control software ASSERT has many valuable features that allow it to reduce the cost of testing wireless networking protocols at scale. This device independent platform for developing and testing wireless network protocols has taken experimentation to a new level!

For more information on the MICA2, click here.
For more information on ASSERT, click here.

The Contiki Operating System - Contiki 2.4 Released

In September we announced that the Contiki team had developed a port for Crossbow's MICAz platform. We are excited to inform our users that the team is proud to announce the release of version 2.4 of the Contiki operating system! Contiki 2.4 brings a number of new improvements over previous versions, several bugfixes, and an overall improved stability of the system. The low-power wireless MAC protocols have gotten an overhaul, improving power-efficiency and improved collision and interference handling. The COOJA/MSPsim simulation environment has received a significant speedup. Many improvements and bugfixes has been made to the uIP code as well as the SICSlowpan implementaion of 6lowpan IPv6-over-802.15.4.

See the changelog for full details and go to the download section to download the 2.4 release!

Environmental Sensing - eKo Style

This past month, Crossbow displayed the award-winning eKo system at the AGU Fall Meeting in San Francisco. The American Geophysical Union (AGU) is a group of members consisting mainly of Earth and space scientists who study how rocks, water, air, space, and life interact with each other to understand how the global system works. What they learn increases our sense of wonder, encourages us to ask new questions, and allows us to make wise use of Earth's many resources. This particular event draws upwards of 16,000 geophysicists from all around the world who meet to review the latest issues affecting the Earth, the planets and their environments. The eKo system provides a key new alternative by which scientists and researchers can gather data from their environment with an ease and efficiency previously unavailable. The event provided a wonderful venue to meet customers, inspire new ideas and most importantly educate others in the use of wireless sensor networks.

The Mebius Sensor Project is aimed at sensing the natural environment of Shonan Fujisawa Campus (SFC). Researchers at SFC have deployed 15 wireless eKo sensor nodes with temperature, humidity and illuminance sensors on the circumference of the campus. Each sensor node has a solar battery panel, and is a part of a larger multi-hop network that covers a broad area. The Mebius Sensor system is designed to be a platform of various ubiquitous computing applications such as enhancing people’s awareness of their natural environment.

Fig.1: Mebius Sensor in SFC

Research Goal

Researchers were determined to create a campus sensing platform for ubiquitous services.

• Services that enhance our awareness of the natural environment
• Services that inspire a discussion on the environment

Challenges

• Easy deployment of the sensor
• Flexible spatiotemporal query of the past sensor data
• Real-time delivery of the sensor data to the services
• Inter-campus cooperation of the sensor networks

Plan and Schedule

The Mebius project plans to cover the whole SFC canpus with 15 sensor poles. Each node has the capability to transmit data up to 2 miles offering a large coverage area and a greater level of detail regarding the various micro environments.

Fig.2: Map of Mebius Sensor in SFC

Available on Twitter

Researchers at SFC have integrated the data collected with popular social media applications such as Twitter. Subscribers can see the temperature on Twitter every hour. Follow mebius_sensor on Twitter.

Fig.3: Mebius Sensor on Twitter

Detail of the Sensor Pole

The Mebius sensor utilized Crossbow's eKo node for its sensor platform. Each node included a solar radiation sensor and a temperature and humidity sensor.

Fig.4: Detail of the Sensor Pole

The Mebius Sensor Project provides a glimpse into basic applications for which eKo has been used. The system can be found frozen in ice, under dense canopies, on tree trunks, bobbing on buoys ... anywhere sensing is needed eKo can be found, silently taking Earth's pulse.

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.

 

Structural Health Monitoring

Part 1 Hardware

Investment in civil infrastructure in the United States alone is estimated to be $20 trillion. Moreover, annual costs amount to between 8-15% of the GDP for most industrialized nations, and this investment is only likely to increase. Much attention has been focused in recent years on the declining state of the aging infrastructure in the U.S., such as the nation's bridges, highways, and buildings. The ability to continuously monitor the integrity of civil infrastructure in real time offers the opportunity to reduce maintenance and inspection costs, while providing increased safety to the public.

Bridges account for a large part of the capital investment in constructing road networks and represent a key element in terms of safety and functionality of the entire highway system. As such, informed bridge management is important to provide the public with timely transportation while maintaining a high level of safety. Manual inspection of bridges costs millions of dollars, is relatively unreliable, and can only be carried out sporadically. Recent years have seen tremendous attention directed toward development of structural health monitoring strategies, with the primary outcome sought being enhanced safety and reliability, with reduced maintenance and inspection costs. Many of the recently constructed bridges have in-depth monitoring systems; for example, the Stonecutters Bridge in Hong Kong which has over 1200 sensors. However, the enormous expense of installing traditional monitoring systems has significantly limited deployment. For example, the total system cost of the monitoring system (including installation) on the Bill Emerson Memorial Bridge in Cape Girardeau, Missouri, USA is approximately $1.3M for 86 accelerometers, which makes the average installed cost per sensor a little over $15,000. Average per node cost for other wired bridge monitoring installations are of a similar magnitude.

The Illinois Structural Health Monitoring Project (ISHMP), based at the University of Illinois at Urbana-Champaign (UIUC), 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 a customizable software framework that simplifies the development of SHM applications for smart sensor platforms. In combination, their sensors and software create an integrated framework that can easily be utilized by most civil engineers without requiring an extensive background in computer science!

ISHMP researchers have focused on Crossbow’s Imote2, because they believe that it to be the only commercially available wireless sensor platform that can meet the high-data throughput demands of SHM applications. The powerful processor and the onboard memory of the Imote2 are the features that set it apart from other wireless sensor platforms and allow its use for the high-frequency sampling required for dynamic structural monitoring. The Imote2 has a low-power X-scale processor (PXA27x) with variable processing speed to optimize power consumption. Moreover, it has 256 KB of integrated RAM, 32 MB of external SDRAM, and 32 MB of flash memory.

The Structural Health Monitoring Accelerometer (SHM-A) sensor board was designed to meet the specific needs of SHM by Prof. Jennifer A. Rice from Texas Tech University as part of her PhD research at UIUC. The board provides flexible and accurate user-selectable sampling rates and anti-aliasing filtering capabilities to account for the local nature of structural damage where higher mode responses of the structure are often required (up to 500 Hz) in addition to low-frequency signals (DC to 20 Hz). To avoid potential signal errors, especially in the higher frequency range, minimizing sample-rate fluctuation (jitter) is critical. While simply interfacing an analog accelerometer with a high-quality ADC could address the sampling rate issues, a programmable signal conditioner was chosen because of the flexibility it provides the user in terms of anti-aliasing and signal processing. The key component of the SHM-A board is the Quickfilter QF4A512, a versatile, 4-channel ADC and programmable signal conditioner with user-selectable sampling rates and programmable digital filters. The board interfaces with the Imote2 via SPI and I2C I/O and has a 3-axis analog accelerometer for vibration measurement, one general analog input, as well as digital temperature, humidity and light sensors. One purpose of the temperature sensor is to provide onboard temperature compensation to the acceleration data. The SHM-A board provides the enabling technology that had been previously lacking to perform structural health monitoring using the Imote2.

Full-scale Implementation

After much testing and lab work, ISHMP researchers moved forward on the full-scale monitoring of the Jindo Bridge in South Korea in June. The Jindo Bridge is Korea's first cable-stayed girder bridge. A joint project between University of Illinois at Urbana-Champaign, KAIST in Korea, and the University of Tokyo in Japan, this deployment constitutes the first dense deployment of a WSN on a cable-stayed bridge. Installed with 70 nodes, the deployment is largest of its kind for civil infrastructure to date.

Prior to deploying the smart sensor network on the Jindo Bridge, the Imote2 and sensor board stacks were housed in enclosures that provided protection against environmental conditions such as rain, wind and dust. The enclosures had a gasketed lid and the ability to mount to the structure. Several nodes were designed to accommodate larger battery sources, while some were designed to utilize integrated solar panels.

Smart sensing technology for structural health monitoring is an emerging field that, by combining civil engineering knowledge with developments in sensor technology, information management, and networking technologies presents a solution that is a robust, significantly lower-cost (~$500/node), alternative to traditional structure inspection techniques. This process of implementing a damage detection and characterization strategy for engineering structures has changed the idea of SHM. The SHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health.

For more details on the Imote2 platform visit Crossbow's site here.

For more information on the SHM sensor board developed at UIUC visit the ISHMP site here and view a detailed report on the development and implementation here.

Stay tuned for Part 2 of the Jindo Bridge Deployment related to the software strategy implemented...

WSNs See Through Walls

Researchers at the University of Utah SPAN lab have found a way to see through walls to detect movement inside a building using wireless sensor networks!

The surveillance technique is called variance-based radio tomographic imaging and works by visualizing variations in radio waves as they travel to nodes in a wireless network. A person moving inside a building will cause the waves to vary in that location, the researchers found, allowing an observer to map their position.

The researchers, electrical engineering graduate student Joey Wilson and his faculty advisor Neil Patwari, have tested the technique with a 34-node wireless network using the IEEE 802.15.4 wireless protocol and TelosB hardware. By “interrogating” the space with signals and multiple receivers, the researchers found they were able to read the waves to detect the location of a moving object within a meter of accuracy.

In a mission-critical application, the researchers envisioned a building imaging scenario similar to the following: Emergency responders, military forces, or police arrive at a scene where entry into a building is potentially dangerous. They deploy radio sensors around (and potentially on top of) the building area, either by throwing or launching them, or dropping them while moving around the building. The nodes immediately form a network and self-localize, perhaps using information about the size and shape of the building from a database (e.g., Google maps) and some known-location coordinates (e.g., using GPS). Then, nodes begin to transmit, making signal strength measurements on links which cross the building or area of interest. The RSS measurements of each link are transmitted back to a base station and used to estimate the positions of moving people and objects within the building.

Locating interior movement from outside of a building is extremely valuable in emergency situations, enabling police, military forces, and rescue teams to safely locate people prior to entering. Variance-based radio tomography is a powerful new method for through-wall imaging that can be used to track the coordinates of moving objects. The cost of VRTI hardware is very low in comparison to existing through-wall imaging systems, and a single network is capable of tracking large areas. These features may enable many new applications that are otherwise impractical.

OceanSense: Realtime Ocean Environmental Sensing

How important are the oceans? Earth is the only planet in the Solar System that has a vast open ocean of liquid water, and it is the ocean that makes our planet uniquely life-bearing. Today, ocean life is regarded by many as more important in the total scheme of things than even plant life on land. There are microscopic plant cells in the top layer of the ocean that produce four times as much oxygen and food as plants on land produce. The bottom line is that there is no life without water and the global ocean is the defining characteristic of our planet. It is the original ecosystem.

Oceanography is of great importance for human beings. For hundreds of years, people have been recording ocean environmental data from scientific expeditions to coastal environmental research. However, even today the only real-time environmental data observation method is through Argo (Array for Real-time Geostrophic Oceanography), which does not come close to meeting the true requirements of oceanography research. Researchers at the Ocean University of China have developed a wireless sensor network for environmental monitoring at sea believing that with the recent progress of sensor networks - it is the tool for the future collection of data for oceanography research.  

Deployed off the seashore near the Moutain LAO of Qingdao, China, OceanSense is a system designed to obtain ocean environmental data in real-time. Using the TelosB Mote platform, the nodes are deployed on the surface of the sea collecting environmental data, such as temperature, light, tide velocity, sensor depth and RSSI from the testbed. The TelosB Motes were encapsulated in bottles and attached to lightweight supporting equipment to make them waterproof as well as raise them 1m above the ocean surface to enable optimal signal strength. To view the process used to create these ocean ready nodes, watch this video that shows the testing, creation and deployment of the OceanSense System.

The OceanSense system has been running for more than half a year, providing environmental monitoring data for further study. The motes communicate with the base station, which transmits collected data to a visualization system running on a database server. Users may then access the data using a browser-based web application that provides a scalar field mapping tool to depict temperature and light intensity as well as a vector field mapping tool to display the tide velocity.

The current system consists of 20 sensor nodes deployed in the field, reporting sensing data continuously to the base station. The complete system is designed to scale to hundreds of sensors covering the sea area off Taipingjiao, Tsingtao. For more information on the OceanSense solution visit the site here and click on the video below for a brief demo of the system and the deployment.

Wireless Sensor Networks Under Fire

Researchers at the University of Corsica off the coast of France released information regarding development efforts they have been conducting dealing with using wireless sensor networks as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. This work was financed by the Territorial Community of Corsica to provide reliable information in fire studies and support fire fighting strategies. To do this a wireless sensor network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; and 3) tracking the fire spread during its spatial and temporal evolution.

Researchers at University of Corsica took on this challenge as the Mediterranean area is a region experiencing strong ecological variations due to global climate change. Wildland and forest fires represent an important cause of natural hazards and disasters for assets and human lives every year. Early detection and accurate tracking of the fire front propagation are key points in fire fighting strategies to minimize the damage and possible casualties. According to fire managers, the delay in detecting a fire ignition in the open is considered as one of the main factors increasing the response time of the first fire fighting actions. The resulting size of the fire to be managed as the final burning area strongly depends on this response time of the first fire fighting action.

One of the great and obvious challenges in performing fire front tracking with WSNs is to avoid the destruction of the Motes by the fire. This effort shows the ability and performance of a Wireless Sensor Network, when the Motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN was then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m².

Using Crossbow's MICA motes and MTS420 GPS enabled sensors, researchers designed the Firesensorsock. The goal of this protection was to dampen the thermal impact from the fire on the Motes during the fire spread and contact. This shield also had to allow both a continuous emission of data and the temperature and the hygrometry inside the sock to vary on short time scales for locating the fire’s position. This innovative technique packaged the Mote and sensors to be thermally protected, preventing their destruction and extending their lifetime beyond the fire event. With this thermal insulation, the network could be reused for subsequent events. The key was to provide a protection able to preserve continuous flow of data from sensors embedded in a thermally destructive environment, namely a large scale natural fire. When sensors were covered with Firesensorsock the temperature and humidity measured during fire experiments was relative to what was measured inside the protection. The Firesensorsock consists of several layers of thermal insulation materials: a layer of simple Zetex fiber, a layer of ceramic wool and a final layer of aluminized Zetex fiber. These layers are fixed to one another with Kevlar thread. This level of protection allowed the sensor to support prolonged fire contact and also wireless communications without any disturbance. The sensing of any significant change of thermal data in the protection, due to the change of external thermal medium governed by the fire allowed for fire detection and tracking.

The sensors were triggered by the arrival of the fire and enabled the researchers to distinguish exactly the direction of the fire’s spread. The estimation of the rate of fire spread was possible because no node was destroyed by the fire in these experiments. It should be noticed that the computed rate of spread is not the same over the whole grid. This feature is consistent with real fire dynamics: indeed, according to infrared video imaging of the horizontal plot, the fire front propagates with a strong asymmetry. The sensing of the thermal data inside the sock which suddenly varies according to the presence of a strong thermal environment, i.e., a flame, leads to the opportunity to detect the presence of a fire. This point was very important for managing fire alerts in a specific areas because it determined the action of the first fire rescue team.

Because it has not been possible to follow the evolution of a fire with current systems available, this new solution was created -  the double system (sensor + Firesensorsock) provided an adapted tool for natural fire scenarios. Results illustrated that the temperature and humidity variations in the socks allowed users to relevantly determine the presence of a fire and becomes a measurement system for the rate of fire spread in the open. The different tests performed during these fire experiments illustrate the ability of the Firesensorsock system to track the fire's evolution!

Environmental Sensor Bus® Capability for eKo System Revealed

ESB® Solution to Easily Integrate External Sensors with Crossbow’s eKo Series

Crossbow Technology, Inc. announced the release of ESB® development platforms for the innovative eKo system at the Coastal Environmental Sensor Networks Conference today. The plug-and-play ESB (Environmental Sensor Bus) architecture provides users with the flexibility to add a wide range of sensors to the eKo system with ease analogous to USB (Universal Sensor Bus), the architecture used worldwide to connect devices to a host computer. The ESB capability allows users to quickly and effortlessly customize the rugged system by integrating smart and custom sensors for their own applications whether it be groundwater contamination or wildfire monitoring.

“Scientists at UMass Boston Center for Coastal Environmental Sensing Networks (CESN) have been researching the development of ‘smart’ sensor networks for observing interactions of coastal systems in order to help manage our coastal resources and solve current and future coastal problems,” said Francesco Peri, Managing Director of CESN. “The ease with which we integrated our own salinity sensors and fluorometers enabled us to start gathering vital data about the areas around Boston Harbor with minimal infrastructure. The eKo real-time system is an ideal platform to bridge the land-water sensor network barrier and is helping us to detect hot spots and hot moments.”

The eKo system is a miniaturized, solar-powered outdoor wireless monitoring system that enables users to quickly and easily set up a wireless communication infrastructure that can be customized to meet their application’s specific deployment needs. The ability to take a solution and use it for pollution detection while at the same time having it deployed for irrigation management showcases the versatility of eKo and the breakthrough it has made for any outdoor wireless monitoring requirement. The new ESB® line features the ES9000, ES9100 and ES9200 all designed to enable the specific interface for the diverse sensors that can be integrated with the eKo solution.

“The applications into which the eKo system can be deployed are virtually limitless,” stated Dr. Alan Broad, Crossbow’s Director of Environmental Products. “With ESB users can interface sensors to monitor pollutants creating chemical fences, measure vibration to verify structural integrity and even set up warning systems for landslides.”

For more information and to order the ESB Development platforms, click here.

For more information on CESN, click here.