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!

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.

EcoWizard: Next Generation Energy Monitoring System

Crossbow Japan Ltd., a subsidiary of Crossbow Technology, Inc., along with strategic partner Sumitomo Precision Products Co., Ltd. announced today the availability of the EcoWizard Wireless Energy Monitoring System. EcoWizard enables users to analyze and monitor energy consumption in real-time using intuitive visualization tools allowing intelligent and efficient energy conservation.

Using leading-edge wireless sensor networking technology EcoWizard offers various energy measurement modes such as watt-hour by RS485, current transformer, and pulse inputs, while significantly reducing the installation costs associated with current solutions. The newly developed data viewer provides numerous options and increased functionality with simple graphical displays of past and present energy consumption (electricity, temperature, etc.).

Crossbow Japan, who has been supplying customized wireless energy solutions to the Japanese market for several years, has now expanded its offering to the public sector with the introduction of EcoWizard. This energy sensing and monitoring system is a cost-effective solution for users in many countries who are now faced with strict energy conservation policies and legislation. The need for a solution that quickly and easily facilitates energy savings is here. Crossbow’s expertise in sensing and wireless network solutions have shaped EcoWizard and launched an advanced system for smart and effortless energy monitoring and conservation.

System Features

• Complete Hardware Monitoring Solution - Sensors for electric power consumption, water, gas, temperature, humidity, light, etc. including pulse input wireless nodes enabling data acquisition from typical gas or water gauges.
• Multi-functional Software Package - The flash-based application is simple to operate, allowing intuitive data viewing. The graphical displays enable identification of total and individual local power consumption instantly. Data can be exported to CSV format, permitting easy report creation. The system offers real-time monitoring and data logging, with built-in alert capability when sensor data exceeds the predetermined threshold.
• Low-cost Installation - Compared to current wired systems, installation or layout changes take a significantly shorter time, leading to increased cost savings. Crossbow's XMesh enables a multi-hopping ad-hoc network between sensor nodes to relay data back to the base station. If the wireless connection is interrupted, the system will automatically re-route to ensure complete data transfer from all nodes to the base station. The sensor nodes are built on the NeoMote product from Sumitomo Precision Products who have deployed over 30,000 nodes for customized industrial sensing.

Pricing and Availability
EcoWizard is priced competitively. The system will be immediately available for purchase in November 2009. Sales inquiries may be directed to sales@xbow.com.

About Crossbow Japan
Established in 2005, Crosbow Japan Ltd. is a joint partnership of Crossbow Technology, Inc. San Jose and Sumitomo Precision Products Co., Ltd. located at the Sumitomo Precision Products’ complex in Amagasaki, Hyogo, Japan. Sumitomo Precision Products has been developing and manufacturing industrial grade wireless sensor networks under the license of Crossbow Technology, whose sales are conducted by Crossbow Japan, Ltd. Founded in 1995, Crossbow Technology, Inc. is the leading end-to-end solutions supplier of wireless sensor networks for diverse applications such as industrial automation, building monitoring, home automation, environmental control, structural monitoring and asset tracking.

Contiki Port for Crossbow's MICAz Mote

Contiki is an open source, highly portable, multi-tasking operating system for memory-efficient networked embedded systems and wireless sensor networks. It has been used is a variety of projects, such as road tunnel fire monitoring, intrusion detection, water monitoring in the Baltic Sea, and in surveillance networks.

Designed for microcontrollers with small amounts of memory and providing IP communication, both for IPv4 and IPv6, a typical Contiki configuration is 2 kilobytes of RAM and 40 kilobytes of ROM. As of yesterday, there was a new Contiki target in the development code: a port for Crossbow's MicaZ Mote, the popular prototyping and research platform for wireless sensor networks. The MicaZ port was developed by Kasun Hewage from the University of Colombo School of Computing, Sri Lanka.

Contiki is developed by a group of developers from industry and academia lead by Adam Dunkels from the Swedish Institute of Computer Science. Dunkels was recently named by Technology Review as one of the Top Young Innovators under 35 in 2009 for his development of minimal wireless-networking protocols that allow almost any device to communicate over the Internet. He wrote the uIP for Crossbow's TelosB Mote platform.

The Contiki team currently has sixteen members from SICS, SAP AG, Cisco, Atmel, NewAE and TU Münich. For more information on this operating system visit the Contiki site here.

The Business Value of Wireless Sensor Networks

Crossbow's advancements in the field of wireless sensor networks was recently featured in the current issue of M2M Magazine. The article deals with the business value of wireless sensor networks and how information derived from WSNs have a powerful effect on the way companies are making decisions in today's world. Here is an excerpt from the article:

A WSN consists of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, or pollutants at different locations. Each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. The size of a sensor node can vary from shoebox-size to the size of a grain of dust.

Even in this economy, companies are finding it easy to make the case for investing in sensor networking. Shana Farley, Marketing Manager, Crossbow Technologies, www.xbow.com, San Jose, Calif., concurs, saying, “Companies will see significant ROI on their WSN investments because the data they are collecting and analyzing is usually in the interest of saving money on consumption, resources, manpower, equipment, etc.”

Crossbow Technology has seen its solution used in typical scenarios such as predictive maintenance on machinery to more unique applications, such as monitoring endangered species, measuring the force of a running horse at the Kentucky Derby, and determining drought conditions in the Sierra Nevada mountain range.

The company’s eKo Pro Series was developed for environmental monitoring. At least four sensors can be attached to one eKo Node. Once customized to a client’s specific monitoring needs, the eKo Nodes form a wireless sensor network. A base radio with standard antenna serves as a collection point, receiving data from the deployed eKo Nodes. Field data is then transmitted to the plug-and-play eKo Gateway, a self-contained embedded Linux-based Web server device. The Gateway is pre-installed with eKoView, a secure Web interface, for viewing real-time data, running reports, and setting up alerts.

For the data to be useful, a reaction must be triggered. “It might be that a perimeter intrusion automatically turns on a camera or that a sprinkler is turned off when a moisture threshold is achieved in a crop field,” says Farley.

Wireless sensor networks are opening up a new world of monitoring, just as wireless phones did for the telecommunications industry. They are enabling companies to transform their businesses. “The main idea behind WSN is that information that was so difficult to collect in the past is so easy to get today,” says Farley. “You can collect data and make smarter decisions. You do not need wires or a person collecting data 24/7. The idea is to connect the physical world with the digital world through wireless sensor networks.”

To view the full article, visit the M2M site here or for more information on Crossbow's wireless sensor networking products, click here. M2M refers to machine-to-machine communications through remote monitoring, RFID, sensor networking, smart services, telematics, and telemetry.

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!

In a BLIP, pervasive IP has arrived.

by Martin Turon, Director of Wireless Software, Crossbow Technology, Inc.

IPv6 was invented in 1998, over ten years ago, yet less than 1% of devices use it.

Why is IPv6 important?

The first answer is "lots of addresses".

Think of your PC and the RAM memory inside. Until recently, 32-bit processors were pervasive, and you couldn't put more than 4GB (4 billion bytes) of memory in them because of the limits in 32-bit address spaces. Now most new computers are 64-bit, and they can address 18 quintillion bytes. The Internet will eventually be forced to "upgrade" its address space as well. Currently over 99% of devices use IPv4 which uses 32-bit IP addresses that are most commonly displayed as 4 bytes in decimal: 192.168.1.100. If you want to host a web server to the world, you typically claim a static IP and one of the 4 billion possibilities is yours forever. But the population of the world is 6.7 billion, so there aren't enough to go around! And what if everyone wants multiple devices that can be uniquely addressed to serve some critical information like a cluster of wireless sensor nodes?

IPv6 is clearly the answer. It provides a 128-bit address space allowing for over 240 undecillion uniquely addressable devices. To put that in perspective, the soon-to-be 6.8 billion people in the world will each be able to have over 300 million subnets with over 18 quintillion devices in each one. That is a total of over 50 octillion uniquely addressable devices per person!  Note: The unallocated address pools in IPv4 are anticipated to be exhausted in 2012.

Which leads to the second answer: "government mandates".

In May 2009, the Federal CIO Council of the US government issued an official roadmap for IPv6 adoption. The report provides a detailed overview of the technology, it's benefits, tips on the various services and how to transition. It also tasks other government agencies with developing concrete plans for how they will deploy IPv6 and requires quarterly review of their progress.

"We can't keep operating in an IPv4 world when we're talking about sensor networks, wireless communications and mobile networks. We need more IP addresses - globally unique IP addresses - and that's what IPv6 provides. We need a target network architecture that's scalable, secure and stable." - Pete Tseronis, Federal IPv6 Working Group Chair and Deputy Associate CIO of the Department of Energy

Since the release of this document, a flurry of articles regarding IPv6 adoption has been written up in the press.

---

How can big IPv6 addresses fit on tiny motes?

Almost in anticipation of the recent uptick in IPv6 adoption, back in March some researchers at UC Berkeley released an Open Source implementation of IPv6 running on TinyOS 2.x called BLIP (Berkeley Low-power Internet Protocol). The BLIP stack runs on mote-class hardware, specifically the TelosB and MicaZ. How did they manage to fit a protocol that uses 128-bit addresses onto a platform with only 4KB of RAM? Well, beyond the fact that Stephen Dawson-Haggerty and other contributors are really really smart, the IETF has had a number of efforts to define ways for IPv6 to run on lossy, low bandwidth links for a while. The IETF timeline follows:

RFC 1883,2460 - IPv6 Specification
RFC 1885,4443 - ICMPv6 Internet control message protocol
RFC 3142 - IPv6-to-IPv4 translation
RFC 3315,4580,4649,4704 - DHCPv6 Dynamic, automatic address assignment
RFC 4861 - Neighbor Discovery
RFC 4862 - Stateless Addr Autoconf
RFC 4944 - IPv6 over IEEE 802.15.4 (6LowPAN) 

Drafts: HYDRO routing (part of ROLL [routing over low-power, lossy links] effort)

Papers: IP is dead, long live IP,  Extended Internet Architecture PhD Thesis by Jonathan Hui

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Why is it cool?

IPv6 running on motes is cool because it really leverages what IPv6 was designed for -- ubiquitous computing.  Imagine a day when all of your appliances and consumer electronics will be able to talk to each other and provide you real time data on their energy usage, health, etc.  The ZigBee alliance has recently signed up to define a specification for an IPv6 stack in addition to an RF4CE stack for remote controls and consumer electronics.  Similar to how WiFi is integrated into all sorts of products today and just works, ZigBee may make it so cheap, tiny devices of the future can provide direct IPv6 connectivity over low-power, low-bandwidth radios.

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What is the status of BLIP?  When will it be released?

BLIP is a work in progress.  It is currently fully supported on the TelosB platform and works on MICAz when compiled in a memory-constrained mode.  It is being folded into the TinyOS 2.x core with a slated release date of late August 2009.  The version in contrib/berkeley/blip is the correct one to use, however, as that one will actually build without manually adding radio stack modifications that are still being negotiated.  IRIS support is in the works as well with the initial port being done by European researchers Miklos Maroti and Lars Schor.  An improved release candidate of BLIP is slated to be pushed into contrib as early as next week.

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Where can the BLIP source code be found?  How do you use it?

1) Download Xubuntos 2.1 VMware image (Howto)
2) Start the VM, login as:
  username: xubuntos
  password: tinyos
3) Start a Terminal
4) Prepare your VM to run BLIP as follows:

- Install IPv6 tools:

    sudo apt-get install netcat6

- Update tinyos-2.x trees from CVS:

    cd /opt
    # hit return for anonymous password
    cvs -d:pserver:anonymous@tinyos.cvs.sourceforge.net:/cvsroot/tinyos login
    cvs -z3 -d:pserver:anonymous@tinyos.cvs.sourceforge.net:/cvsroot/tinyos co -P tinyos-2.x
    cvs -z3 -d:pserver:anonymous@tinyos.cvs.sourceforge.net:/cvsroot/tinyos co -P tinyos-2.x-contrib/berkeley

- Add a simple environment setup script

cd /opt/tinyos-2.x-contrib/berkeley/blip
cat <<-EOF > setenv
export TOSROOT=/opt/tinyos-2.x
export TOSDIR=/opt/tinyos-2.x/tos
export LOWPAN_ROOT=$PWD
export TOSMAKE_PATH="$LOWPAN_ROOT/support/make"
EOF

- Build the driver

    cd /opt/tinyos-2.x/support/sdk/c/sf
    ./bootstrap
    ./configure
    make
    cd /opt/tinyos-2.x-contrib/berkeley/blip/support/sdk/c/blip
    make

- Patch motelist to handle mib520 for micaz discovery

cd ~
cat <<-EOF > motelist-linux.mib520.patch
--- motelist-linux	2006-12-12 10:23:01.000000000 -0800
+++ /usr/bin/motelist	2009-05-19 11:28:26.000000000 -0700
@@ -61,7 +61,10 @@ sub scan_sysfs {
 
 # Scan /sys/bus/usb/drivers/usb for FTDI devices
 my @ftdidevs =
- grep { (\$_->{UsbVendor}||"") eq "0403" && (\$_->{UsbProduct}||"") eq "6001" }
+ grep { (\$_->{UsbVendor}||"") eq "0403" && 
+ (((\$_->{UsbProduct}||"") eq "6001") || 
+ ((\$_->{UsbProduct}||"") eq "6010")) 
+ }
 map { {
 SysPath => \$_,
 UsbVendor => snarf("\$_/idVendor",1),
EOF
cd /usr/bin
sudo patch -p0 < motelist-linux.mib520.patch

- Download Blip.micaz.patch

 - Patch the blip contrib sources to add automatic micaz support
and fix some build issues.

    cd /opt/tinyos-2.x-contrib/berkeley/blip
    patch -p0 < ~/blip.micaz.patch

5) Use your prepared VM to build and deploy BLIP:

- Enable the BLIP build environment:

    cd /opt/tinyos-2.x-contrib/berkeley/blip
    source setenv

- Build the UDPEcho app and base station for telosb and micaz:

    cd /opt/tinyos-2.x-contrib/berkeley/blip/apps/UDPEcho
    make telosb blip
    make micaz blip

    cd /opt/tinyos-2.x-contrib/berkeley/blip/apps/IPBaseStation
    make blip telosb
    make blip micaz

- Connect a TelosB via USB, and flash it with the base station code
(Install FTDI drivers if needed)
(Accept TelosB device when dialog appears)
(Right click USB device in lower right of VM and select connect)

    motelist # find ttyUSB port to use
    cd /opt/tinyos-2.x-contrib/berkeley/blip/apps/IPBaseStation
    make blip telosb install,64 bsl,/dev/ttyUSB0

- Connect a MicaZ via USB, and flash it with the remote node code
  (Install FTDI drivers if needed)
  (Accept MIB520 device when dialog appears)
  (Right click USB device in lower right of VM and select connect)

 	 
    motelist # find ttyUSB port to use
    cd /opt/tinyos-2.x-contrib/berkeley/blip/apps/UDPEcho
    make blip micaz install,1 mib520,/dev/ttyUSB0

6) Fire up Linux network driver to communicate with BLIP network:

- Start the ip-driver

    cd /opt/tinyos-2.x-contrib/berkeley/blip/support/sdk/c/blip
    sudo ./ip-driver /dev/ttyUSB0 telosb
    # password: tinyos	 
    # blip console commands:
    # blip:xubuntos-tinyos> help
    # blip:xubuntos-tinyos> log WARN
    # blip:xubuntos-tinyos> routes	

7) Use standard IPv6 tools to access the remote BLIP nodes:

    # Ping - base station and remote node
    ping6 2001:470:1f04:56d::64
    ping6 2001:470:1f04:56d::1

    # Access shell on remote node
    nc6 2001:470:1f04:56d::1

Note that BLIP ships with support for pinging a node with standard the ping6 tool, shelling into a node with netcat (the shell is a custom one, not ssh, type help to see available commands), and over-the-air reprogramming of nodes with a deluge-like interface.  BLIP is an exciting software development for the TinyOS community and users of TelosB, MICAz, and soon IRIS platforms.  We encourage you to try it and track its rapid development.

Martin Turon is Crossbow's Director of Wireless Software. Not only an expert in the field of wireless sensor networks, Martin has been instrumental in simplifying the WSN user experience with advancements in interface and server tools using his background in video game design, mobile phone software and operating systems. He is the current Chair of the ZigBee WSN group which is working to establish the standard for low-power routing while leading the Wireless software development team at Crossbow for future product enhancement. Martin obtained degrees from University of California, Berkeley in Electrical Engineering and Computer Science. He has also studied Artificial Intelligence at University of California, Los Angeles and received a certificate in Math for Financial Engineering from Haas Business School. Martin is an avid lover of indie rock and performs with various ad-hoc musical projects.

Landslide Detection for Mountainous Regions

The Times of India reported today on a wireless sensor solution developed by students of Amrita Vishwa Vidyapeetham University for landslide detection. This development effort used Crossbow's MICAz Mote platform and was done in collaboration with the European commission and Indian Space Research Organisation (ISRO). This pilot deployment of India's first landslide detection system with wireless sensor networks was put in place at Munnar, Idukki, Kerala, India. This implementation brought together scientists from diverse fields such as geology and geophysics, mechanical,computer, electrical, electronics, and communication engineering to save human lives, preserve the environment, and mitigate property damage.

The devastation and loss of life caused by landslides affects hundreds of people every year around the world. Amrita University's rainfall induced landslide detection system uses a heterogeneous network that included wireless sensor networks in combination with Wi-Fi and satellite technology. The pilot site chosen for this study is highly prone to landslides due to systemic monsoon induced rainfalls in the region.

"Landslides occur frequently in mountainous terrains, especially during monsoons but detecting them in advance is not an easy task,'' says Dr P Venkat Rangan, vice chancellor, Amrita university. An expert in wireless communication, Rangan led a team of students in developing the model that has become operational in Munnar town in the Idukki district of Kerala.

This breakthrough technological system was developed as part of the research project WINSOC (Wireless Sensor Network with Self-Organisation Capabilities for Critical and Emergency Applications). Wireless panels with sensor nodes to read different soil parameters such as moisture, vibration and movement were embedded 15 metres beneath the earth at different points, says Maneesha Ramesh, a faculty member, who was part of the project.

The actual deployment site in the Idukki district built on the existing setup at Munnar as it provided the infrastructure needed for retrieving geological and hydrological data from the field as it was necessary for the data to be transmitted a long distance for further analysis. The data received from the geophysical sensors were transmitted through the wireless sensor network which used a two layer hierarchical topology.

The sensors were attached to a wireless transmission device, in this case Crossbow's MICAz Mote platform, which would then convert the analog value into a digital value and send the inputs to the base stations, which were connected to Amrita mutt's Kollam campus. "Experts will be monitoring the inputs from the base stations in real time and any unusual behaviour or extreme value will trigger an alarm,'' says Dr Rangan.

Multiple sets of geophysical sensors are located in a distributed manner inside a column, referred to as the 'sensor column'. The sensor columns are approximately 5 -6 meters long and are buried deep inside the earth and the data from them are retrieved using lower layer wireless sensor nodes attached to the sensor columns.

The lower layer wireless sensor nodes were wirelessly connected to a hierarchy of upper level wireless nodes that would forward the data on to a Gateway. The data was then sent via a directional Wi-Fi link to a Field Data Management Center (FMC). The data was then forwarded over a satellite link to the Data Management Center (DMC) which has sophisticated landslide data processing and modeling capability, located at Amrita University, Amritapuri campus which is situated approximately 252 kilometers away from the deployment field.

The fully tested model has become operational in Munnar. The system can be deployed in any part of the country prone to landslides and snow avalanches. The application could also be put to industrial use for the study of gas leakages or in conservation of forests by early identification of forest fires during summer.

As part of this project, representatives from various European partners like University of Rome, Selex Communications, Intracom Telecom, Czech Centre for Science and Technology arrived at the Amrita University to learn about the first-ever wireless sensor network system for landslide detection. The Amrita wireless sensor network system for landslide detection has been developed as part of WINSOC which is co-funded by INFSO DG of European Commission.

Testbed Testing

Wireless sensor network testbeds are critical for understanding and meeting the technical challenges of networks deployed in real world scenarios. Hardware and software testbeds have become the preferred basis for experimenting with embedded wireless sensor network applications. They provide a means for developing and evaluating sensor network technology in a controlled and instrumented environment. Experimentation with current hardware and software platforms, allows users to not only demonstrate applicability in real environments but also to validate prototypes. Compared to field deployments, the testbeds yield substantial efficiency in instrumenting potentially long-lived experiments, which is valuable in the debugging, validation, and integration phases of reliable wireless sensor networks. Universities and labs across the world have set up networks of hundreds of nodes using a Mote platform from Crossbow's suite of wireless sensor network devices choosing from simple platforms such as the TelosB to advanced devices like the Imote2.

Researchers at the University of Buffalo, SUNY and Georgia Institute of Technology have written an article focused on the the idea of taking wireless testbeds to the next level by incorporating multimedia and characterizing the challenges of wireless multimedia sensor networks (WMSNs). The availability of low-cost hardware has enabled the development of WMSNs, i.e., networks of resource-constrained wireless devices that can retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment, along with standard wireless sensor networks. Research is being conducted on prototypes of multimedia sensors and their integration into testbeds for experimental evaluation of algorithms and protocols for WMSNs. Open research issues and future research directions, both at the device level and at the testbed level, are discussed and tested constantly.  Network testbeds allow the effectiveness of algorithms and protocols to be evaluated by providing a controlled environment for measuring network performance.

Every testbed utilizes a specific Mote platform that is optimized for that particular testbed's focus. The wireless sensor platform is chosen based on its available capabilities to allow for multimedia integration. A WMSN is a distributed wireless system that interacts with the physical environment by observing it through multiple media. Furthermore, it can perform online processing of the retrieved information and react to it by combining technologies from diverse disciplines such as wireless communications and networking, signal processing, computer vision, control, and robotics. Applications in the real world that would benefit from WMSNs were categorized into the categories of surveillance, traffic monitoring and enforcement, personal and health care, gaming, and several elements of environmental and industrial monitoring by researchers at SUNY, Buffalo and Georgia Tech. Testbeds allow the observation of the performance of the WMSN in a controlled environment. Hence, the effect of different types of inputs, physical operating conditions, and subjects for sensing can be studied, and the functioning of the devices in the testbed may be changed appropriately for accurate measurement. A few WMSNs developed are outlined below:

A visual sensor testbed was developed as part of the Meerkats project to measure the tradeoff between power efficiency and performance. Results revealed that there was significant energy consumption in keeping the camera active, and writing the image to a Flash memory followed by switching the camera off conserved energy. There was also a finite instantaneous increase in the energy consumption due to state transients. Researchers were also able to determine that the processing-intensive benchmark resulted in the highest current requirement, and transmission was shown to be only about 5% more energy-consuming than reception.

Expandable, vision-, and sensor-equipped wireless robots with MICA sensor motes for networking were designed in the Explorebots testbed architecture. The target localization experiments on the testbed, composed of these mobile robots, used on board multimedia sensors such as custom-designed velocity and distance sensors, motor movement control, an in-built magnetic two-axis compass, and sonic sensors. By processing the sound and light sensors outputs, the robots were guided towards the target source.

The Mobile Emulab network testbed provided a remotely accessible mobile wireless and sensor testbed. Robots carried motes and single-board computers through an indoor field of sensor-equipped motes. A remote user could position the robots, control all the computers and network interfaces, run arbitrary programs, and log data in a database. The path of robots, which was also equipped with Webcams, could be planned, and a vision-based system provided positioning information with an accuracy within 1 cm.

IrisNet (Internet-scale resource-intensive sensor network services) was an example software platform for a heterogeneous WMSN testbed. Video sensors and scalar sensors were spread throughout the environment and collected potentially useful data. IrisNet allowed users to perform Internet-like queries to video and scalar sensors that spread throughout the environment. The architecture of IrisNet was two-tiered: heterogeneous sensors implemented a common shared interface and were called sensing agents (SAs), while the data produced by sensors was stored in a distributed database that was implemented on organizing agents (OAs). Different sensing services were run simultaneously on the architecture. For example, a set of video sensors could provide a parking-space finder service, as well as a surveillance service.

In the design and implementation of SensEye, a multiple-tier network of heterogeneous wireless nodes and cameras, was created to test surveillance applications. Each tier comprised nodes equipped with similar cameras and processing ability, with increasing resolution and performance at each stage. The lowest tier consisted of low end devices, i.e., MICA2 Motes equipped with 900 MHz radios interfaced with scalar sensors, e.g., vibration sensors. The second tier was made up of motes equipped with low-fidelity Cyclops or CMUcam camera sensors. The third tier consisted of Stargate nodes equipped with Webcams that could capture higher fidelity images than tier 2 cameras. Tier 3 nodes also performed gateway functions, as they were endowed with a low-data-rate radio to communicate with motes in tiers 1–2.The aim was to efficiently undertake object detection, recognition and tracking by triggering a higher tie into the active state based on a need basis.

The WMSN-testbed at the Broadband Wireless Networking (BWN) Laboratory at Georgia Tech was based on commercial off-the-shelf advanced devices and had been built to demonstrate the efficiency of algorithms and protocols for multimedia communications through wireless sensor networks. The testbed was integrated with the scalar sensor network testbed, which was composed of a heterogeneous collection of Imote and MICAz motes from Crossbow. The testbed allowed the integration of heterogeneous devices in experimental testbeds and some succesful examples in developing APIs and system software for WMSNs.

These are just a few of the various wireless sensor network testbeds found worldwide. Crossbow's vast portfolio of wireless sensor platforms provides researchers and government/commercial users with the equipment they need to set up a lab for their wireless sensor course or to verify their specifications prior to real world deployment of their wireless sensor network. For information on how to set up your own WSN testbed or for details on Crossbow's wireless sensor network platforms, contact sales@xbow.com.

Professors design AK47-locating 'smart dust' helmets

Last week Vanderbilt University's inhouse Exploration newsletter reported on the gunshot-locator net developed by the university's Institute for Software Integrated Systems (ISIS). Crossbow's Mote platforms were once again highlighted in their use for the shooter location system. Acoustic gunshot detectors have become common in the past few years, and some have been reduced in size to where a single soldier can wear one on his uniform and be cued-in to an enemy's location as soon as he fires. Engineers at ISIS have developed a system that can give soldiers just such an edge by turning their combat helmets into "smart nodes" in a wireless sensor network.

Current systems rely on centralized or stand-alone sensor arrays. This limits their accuracy and restricts them to identifying shooters at line-of-sight locations. By contrast, the ISIS system combines information from a number of nodes to triangulate on shooter positions and improve the accuracy of its location identification process. It also uses a patented technique to filter out the echoes that can throw off other acoustic detection systems, explains Akos Ledeczi, the senior research scientist at ISIS who heads up the development effort.

ISIS developed this novel technology with the support of the Defense Advanced Research Project Agency and the university has patented the system's key elements. "When DARPA gave us the assignment of creating a shooter location system using nodes with very limited capabilities, they didn't think we could solve the technical problems," Ledeczi admits. "At first, I didn't think we could do it either, but we figured out how to make it work!"

Retired U.S. Army Lieutenant Colonel Albert Sciarretta, who assesses new military technologies in urban environments for DARPA, is one of the experts who is impressed by the ISIS system: "It's strong points are that it isn't limited to locating shots fired in direct line-of-sight, it can pick up multiple shooters at the same time, and it can identify the caliber and type of weapon that is being fired."

"Because the microphones on the helmet are so close together, the precision is not very high," Ledeczi says. "However, the nodes are continuously exchanging the times and angles of arrival for these acoustic signals, along with their own locations and orientations. When two or more nodes detect the shot, they can provide the bearing with better than one degree accuracy. The range is typically within a few meters even from as far as 300 meters. The more sensors that pick up the shot, the more accurate the localization." The ISIS system communicates its findings with the personal digital assistants that the soldiers carry. The PDAs are loaded with maps or overhead pictures of the area upon which the shooter locations are displayed.

In each package is a wireless network node, of a type dubbed a "smart-dust mote" for its small size and cheapness. There are also four separated microphones, for picking up the acoustic signatures of flying bullets, and a GPS satnav location system. The GPS isn't accurate enough to act as a basis for properly pinning down opposing gunmen, so the Vanderbilt boffins added a crafty radio interferometry enhancement system of their own - apparently of such cunning that it has attracted as much interest as the rest of the system on its own.

The system works by picking up the distinctive conical shockwave trailing behind a passing supersonic bullet - the same phenomenon which produces a sonic boom behind a plane at Mach 1+. This is then related to the muzzle blast from the weapon which fired it, trailing slightly behind (the two noises are heard by people under fire as "crack-thud", or "crack-bang"). A software algorithm in the unit can work out a range and bearing to the enemy weapon's muzzle. A video of the trials can be seen on the Exploration site:

The ISIS shooter system uses the wireless nodes produced by Crossbow Technology Inc. These smart nodes, or motes, form self-organizing wireless-sensor networks and are the realization of the Pentagon's "smart-dust" concept of radically reducing the size and cost of sensor networks for military applications. Current commercial shooter location systems are extremely expensive, with prices ranging from $10,000 to $50,000 per unit. By contrast, an entire node for the ISIS system weighs only slightly more than the four AA batteries that power it and costs about $1,000 to construct using currently available commercial hardware.

The Exploration article is here, and a detailed paper from Ledeczi's team here.