Mote Musings

October 28, 2008

Call for Papers: the 5th IEEE/ACM International Conference on Distributed Computing in Sensor Systems (DCOSS '09)

DCOSS is one of the premier conferences for sensor network research. It is intended to cover several aspects of distributed computing in sensor systems such as high level abstractions and models, systematic design methodologies, signal and information processing, algorithms, analysis and applications. Many of those who will be demonstrating are featured Crossbow customers.

Distributed sensor systems have become a highly active research area due to their potential for providing diverse new capabilities. Such systems allow intelligent dense monitoring of physical environments. The focus of this conference is on distributed computing issues in large-scale networked sensor systems (including algorithms, applications, systematic design techniques and tools, and in-network signal and information processing).

June 7 - 10, 2009, Marina Del Rey, USA                

IMPORTANT DATES
Submission Deadline: 11:59PM EST Jan 25, 2009
Notification: March 24, 2009
Camera Ready: March 31, 2009

Detailed submission guidelines coming soon on http://www.dcoss.org

Authors are invited to submit original unpublished manuscripts that demonstrate current research on computational aspects of distributed sensor systems. Topics of interest include but are not limited to:
  • Computation and programming models
  • Energy models, minimization, awareness
  • Distributed collaborative information processing
  • Detection and tracking
  • Theoretical performance analysis: complexity, correctness, scalability
  • Abstractions for modular design
  • Fault tolerance and security
  • Languages, operating systems
  • Task allocation, reprogramming and reconfiguration
  • Dynamic resource management
  • Scalable, heterogeneous architectures (node and system-level)
  • Middleware interfaces, communication and processing primitives
  • Design, simulation and optimization tools for deployment and operation
  • Design automation and application synthesis techniques
  • Closed-loop control for sensing and actuation
  •  Case studies: lessons from real world deployments
  • Network coding and compression
The conference will be co-located with several closely related workshops,and will provide a forum for researchers and practitioners to present their contributions related to the above high-level aspects of distributed sensor systems. In addition to contributed papers, the meeting will also include keynote addresses by leading researchers, a panel discussion, and a poster session.

For more information click here.

October 07, 2008

The State of Wireless Sensor Networks

The continuous size and cost reduction of electronic devices is gradually making the vision of ubiquitous wireless sensors networks a reality. After almost a decade of extensive research, Wireless Sensor Networks (WSNs) are in the midst of the transition towards industrial deployment in various application domains such as automotive, environmental monitoring, health care, energy management, and building and industrial automation. BAIA presents a panel of outstanding experts from the academia and the industry who have played an essential role in the history and development of WSNs, including Crossbow's President/CEO, Mike Horton.

BAIA has organized an outstanding panel that will explore the state of wireless sensor networks on the evening of October 8th at UC Berkeley. Panelists include:

  • Prof. David Culler, UC Berkeley, CTO and Co-Founder Arch Rock
  • Mike Horton, CEO and Co-Founder Crossbow
  • Prof. Raju Pandey, UC Davis, CTO and Co-Founder Synapsense
  • Prof. Kris Pister, UC Berkeley, CTO and Co-Founder Dust Networks
  • Dr. Joe Polastre, CTO and Co-Founder Sentilla
  • Prof. Alberto Sangiovanni-Vincentelli, UC Berkeley, CTA and Co-Founder Cadence Design Systems

Questions addressed will include:

- What applications will drive the mass deployment of WSNs both in the short and in the long term?
- What players will be most successful in the WSN domain and what business model will they adopt?
- What are the main barriers before wide adoption of WSNs?
- When will the deployment of WSNs happen in large volumes?

The event is free, but limited to 100 attendees. Learn more and register here.

September 02, 2008

San Francisco Chronicle Features Crossbow's eKo System

Stagecoach.eKo.Node Crossbow's eKo system has triggered an agricultural revolution in the world of precision agriculture and environmental monitoring. This cutting edge system was recently featured in the San Francisco Chronicle and the story can be viewed here.

(08-31) 15:50 PDT -- On a rolling hillside planted with row upon row of Cabernet grapes, viticulturist Jason Cole waxes eloquent about the elusive notion of 'terroir,' a term French farmers use to describe the 'je ne sais quoi' of crops harvested in any given locale.

"Grapes, chocolates, coffee, these are all incredibly good at soaking up their environments and spitting them out in their fruits," said Cole, who oversees the preening and pampering of more than 500 acres of vines planted at the Stagecoach Vineyard in Napa County.

That vineyard is a test bed for a new wireless sensing technology that measures soil wetness, wind speed, temperature and humidity to take the statistical pulse of the vineyard's microclimates to help determine how often and how much to irrigate. The system being tested at Stagecoach was developed by Crossbow Technology, a privately held, 90-person San Jose company that has created inertial guidance sensors for the aviation industry and researched the use of wireless sensor networks for the federal Defense Advanced Research Projects Agency. Other manufacturers of microclimate sensing systems include the Austrian company Adcon Telemetry, as well as Ranch Systems of Novato and Grape Networks of San Ramon.

The sensors that Cole is using at Stagecoach Vineyard represent one manifestation of a broader phenomenon called precision agriculture - the attempt to tailor the cultivation of large stretches of land so that the smallest possible subsection of a farm gets special but automated attention. In the Midwest, with its amber waves of grain, precision agriculture has been synonymous with huge tractors equipped with global positioning systems to keep the rows straight, for instance. But in California, the land of fruits, nuts and other specialty crops, precision agriculture has been expressed in technologies such as Cole's efforts to use wireless sensors to compute 'terroir.'

"The way that growers for many years decided whether it was time to water was they stuck their thumb in the ground," said Robert Robinson, vice president for Crossbow's wireless sensor division.

The basic field kit that Crossbow released earlier this year, priced at $3,359, consists of three sensing nodes that feed data collected in the field through an electronic gateway into what is essentially a Web page that can be viewed from any Internet-connected device. Crossbow says that basic configuration can divine the microclimate of sites as varied as a 4-acre plot of land in hilly and varied terrains such as Napa and 20 acres in the flatter, homogeneous Central Valley. Additional kits can extend the sensing network, wirelessly and indefinitely, over hill and dale.

Moisture sensors
Kneeling alongside a vine at Stagecoach Vineyard, Cole explained how the system, in addition to measuring temperature and humidity with above-ground sensors, sticks a virtual thumb deep into the soil in the form of two moisture sensors, one at a depth of 1 foot and the other at 3 feet.

Stagecoach.eKo.Cole

"The whole point is to monitor what the roots are experiencing," Cole said. "Watering grapes is one of the most important factors to wine quality. You want to stress the vines in order to condense the flavor into smaller berries."

UC Davis Professor Stu Pettygrove, a soil specialist who has tracked precision agriculture in California, said the water-sensitivity of wine grapes, coupled with their high value relative to other agriculture products, make them a good candidate for this high-tech approach. But how many other California crops fit that description? Pistachios were the only other example Pettygrove offered. He said water-stinginess at just the right point helps burst the shells, making pistachios easy to eat.

Tree crops experiment
Stagecoach.eKo.Node.View Professor Michael Delwiche, chairman of biological and cultural engineering at UC Davis, has experimented with wireless sensing systems that precisely apply water - sometimes mixed with chemical fertilizers in a process called fertigation - to tree crops like nectarines. So far, however, the cost benefit is not there in production orchards, he said.

Delwiche said wireless sensing systems and precision watering might find a home in commercial nurseries and flower-growing greenhouses, where the impetus is not purely economic - as measured by greater crop value - so much as it is regulatory. "They are under environmental regulation not to have runoff from the nursery location," Delwiche said. Eventually, manufacturers will try to improve the performance and bring down costs to encourage broader adoption of wireless sensing systems, he said. Meanwhile, the technology remains economical in niche markets - or exceptionally arid locales.

"In Israel, where water is so dear and they have the technological infrastructure, they're doing a lot of work in this area," Delwiche said. But at Stagecoach Vineyard, where cachet is central to the business plan, the cost of wireless sensing technology is hardly a barrier to the pursuit of quality.

"We're trying to grasp the 'terroir', but you'll always be grasping, you'll never have it all," Cole said.

For more information on the eKo system, visit the eKo site here.

August 20, 2008

Extending Our Senses into the Physical World

The picture of a future with wireless sensor networks-webs of sensory devices that function without a central infrastructure--is quickly coming into sharper focus through the work of Los Alamos National Laboratory computer scientist Sami Ayyorgun. Using Crossbow's TelosB Motes in their research, proponents of this new technology see a world with deployments to improve a wide range of operations.

LANL.TelosB Engineers could wirelessly monitor miles of gas and oil pipelines stretching across arid land for ruptures, damage, and tampering. Rescue workers might detect signs of life under the rubble of a collapsed building after an earthquake, thanks to a network of sensors inside the structure. Armed forces could keep an eye on a combat zone or a vast international border via a sensor network that could promptly provide alerts of any intrusion or illicit trafficking.

"It's not easy to envision the impacts that sensor networks will make, both socially and economically," Ayyorgun said. "Like many other researchers, I think they are likely to rival the impact that the Internet has made on our lives."

Ayyrogun has developed a new communication scheme that brings the reality of these and other applications a step closer. He has shown for the first time that concurrent gains in many measures of performance are possible, including connectivity, energy, delay, throughput, system longevity, coverage, and security.

In recognition of the multifaceted improvements Ayyorgun's research makes on state-of-the-art technology in this field, his recent paper, "Towards a Self-organizing Stochastic-Communications Paradigm for Wireless Ad-hoc/Sensor Networks," has been nominated for the Best-Paper Award from a pool of more than 250 manuscripts at the International Conference on Mobile Ad-hoc and Sensor Systems (MASS) of the Institute of Electrical and Electronics Engineers (IEEE). Ayyorgun will present the paper at this prestigious meeting of the IEEE beginning September 29, in Atlanta, Georgia.

Like cell phones, wireless sensor networks depend on small, independently powered devices, often called motes, to communicate. But unlike cell phones, which always relay their signal through a base station such as a tower, multihop sensor motes use each other to relay signals, transmitting communiqués through a series of "hops" from one mote to the next. Without the need to build a mesh of base stations that must be wired or have a substantial supply of energy, creating information-bearing ad-hoc networks to suit each unique set of circumstances would significantly reduce costs.

"Wiring or 'beefing up' system resources is expensive and is often not feasible for many applications," Ayyorgun said, calling that a "major impetus" for wireless network research. But with nearly all motes dependent on a portable source of power like a battery, it is important that the devices be as energy efficient as possible. "Energy efficiency is a first-class design criterion," he said.

TelosB And energy utilization isn't the only consideration. Other performance aspects of concern include the system's connectivity; the delay, or time it takes for data to be transported; the throughput, which measures the amount of data the system can handle at once; and network security, to name a few. Many solutions aimed at advancing wireless sensor networks have managed to improve performance over at most a few metrics at the expense of others. Ayyorgun analogizes the conundrum to a Rubik's cube, the cube-shaped toy in which the aim is to match each of the six sides with one distinct color. Often, gains in one aspect of wireless sensor network performance such as energy efficiency have only been achieved with losses in another area, such as the end-to-end delay.

With Ayyorgun's scheme, however, "all of the colors have started to match," he said. The sensor network was more energy efficient with shorter delay times, and the other performance considerations mentioned earlier have all improved as well.  "The motes communicate randomly, but their random behavior-their genetic code, if you will-has collective intelligence by design," he said. That collective intelligence results in the concurrent performance gains over many aspects, he added.

"We have good colors on all sides, but it's not perfect yet," Ayyorgun said, emphasizing that wireless sensor networks are still in the development stage. Many issues remain to be addressed, just as we are beginning to realize the potential of these "networks of the future."

Ayyorgun acknowledges the support of the Laboratory Directed Research and Development Office at Los Alamos, the Los Alamos Engineering Institute, the Center for Nonlinear Studies, and colleagues, as well as his students.

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is operated by Los Alamos National Security, LLC, a team composed of Bechtel National, the University of California, The Babcock & Wilcox Company, and Washington Group International for the Department of Energy's National Nuclear Security Administration.

Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

April 29, 2008

Imote2.Builder Kit featured in InfoWorld

Infoworld InfoWorld has reviewed and featured Crossbow's Imote2.Builder kit in an article last week. The kit was reviewed by Strategic Developer Martin Heller who stated that, "The Imote2.Builder Kit makes creating wireless sensor networks a snap." From his article:

Imote2hardwareconfig_5 I spent several hours today exploring the Crossbow Imote2.Builder Kit, a "complete development environment for high performance wireless sensor networking (WSN) applications leveraging the Microsoft .NET Framework," as the company describes it.

(I'd never say "leveraging" and "Microsoft" in the same sentence myself if I could avoid it, because of Microsoft's rather checkered legal history of "leveraging" its near-monopoly -- but oops, I did it again. Back to Crossbow.)

The Imote2 .Builder Kit sells for $990 in the U.S. in small quantities. It includes three Imote2 processor boards, two Imote2 sensor boards, two battery boards, batteries, a USB cable, and software on CD-ROM. Obviously, individual boards are cheaper, especially in quantity.

Why so many boards? The processor boards also have radios, and can talk to each other using the 802.15.4 protocol. The Imote2 has an XScale CPU @ [13–416] MHz and a DSP, 256kB SRAM, 32 MB of SDRAM and 32 MB of FLASH, and baker's dozen of I/O ports of various stripes in addition to the radio and antenna. It has two pairs of connectors for sensor boards, a set for a "basic sensor board" on one side and an "advanced sensor board" on the other side. The flash image includes the .NET Micro Framework.

The sensor boards that come with the kit are of the basic variety, but I guess that refers to the connector they use: they actually have a 3d Accelerometer, an advanced temp/humidity sensor, a light sensor and 4 channel A/D.

The Imote2 software is an add-on to Microsoft Visual Studio 2005 (yes, 2005, not 2008) and .NET Micro Framework 2.0 (yes, 2.0, not 2.5). A 90-day trial version of Visual Studio 2005 Professional is provided with the kit.

I found the programming model for the Imote2 easy to understand, as I was already fluent in C# and familiar with Visual Studio 2005 and the .NET Micro Framework. I think I could build wireless sensor network applications with this kit very quickly: in days to weeks, depending on the complexity of the application.

The processors seem plenty fast. Debugging is trivially easy. The only trouble I had with the kit was a minor but annoying deployment issue: sometimes a board would stop taking downloads, and code deployment from Visual Studio would fail. I was always able to recover from this by stopping all the software on the PC that talked to the board, disconnecting the board from the USB bus, reconnecting and resetting the board, and restarting the software.

MotePlotAccording to the company, this is most likely a problem with the Microsoft USBSPOT driver. Once I had a board programmed, it would be fine.

The picture at the top of this article is the hardware configuration for the most advanced demo in the kit, a star network in which two battery-powered CPU/sensor stacks transmit accelerometer data, one CPU board receives the signals and sends them over the USB cable, and the PC plots the live output, as shown at left. Overall, this is a very impressive kit.

Martin Heller is a Web and Windows programming consultant, is a Contributing Editor for InfoWorld. He develops databases, software and sites, and writes, edits and consults from his office in Andover, Massachusetts, as he has for over 20 years. For more information on Crossbow's Imote2.Builder kit, please contact sales or visit our website here.

April 24, 2008

Intelligent Transportation Systems

Itsjetsons When I think about the future of transportation, the image that pops into my mind is the world of the Jetsons - a world of automation and intelligent transportation systems. A place where machines have the ability to sense and understand their surroundings to allow for a safer more efficient transportation environment. With major innovations slowly taking place all around us, we do not realize how quickly Intelligent Transportation Systems (ITS) are becoming part of our every day life. Implementations such as FastTrack to stop delays at bridge tolls, new technology in vehicles such as the Lexus LS 460 with advanced parking guidance so the car can park itself, Land Rover with adjustable suspension TerrainResponse™, or the Infiniti with wireless connectivity...all these innovations lend themselves to creating more intelligence in the transportation sector.

Moteiris What is an intelligent transportation system? The term refers to efforts to add information and communications technology to transport infrastructure and vehicles in an effort to manage factors that typically are at odds with each other, such as vehicles, loads, and routes to improve safety and reduce vehicle wear, transportation times and fuel consumption. The last few years have seen the emergence of many new technologies that can potentially have major impacts on Intelligent Transportation Systems. A recent study by the UK Governments Office of Science and Innovation, which examined how future intelligent infrastructure would evolve to support transportation over the next 50 years looked at a range of new technologies, systems and services that may emerge over that period. One key class of technology that was identified as having a significant role in delivering future intelligence to the transport sector were wireless sensor networks (Motes) and in particular the fusion of fixed and mobile networks to help deliver a safe, sustainable and robust future transport system based on the better collection of data, its processing and dissemination and the intelligent use of the data in a fully connected environment. Motes can also be augmented with additional sensors – such as those for detecting light, temperature and acceleration – hence enhancing their features and making their application areas virtually limitless. It is generally perceived that Motes will become the low-cost, ubiquitous sensor of the future, especially once its size shrinks dramatically to merit its name.

Itsnulogo_2Researchers at Newcastle University have been at the forefront of looking into the technology challenges of using these small, low-cost and smart wireless sensors in transport and the application areas where they could be employed. It is clear to the ITS community that the emergence of low cost sensors will open up new paradigms in how we can pervasively collect data from sensors, convey information along fixed and mobile low cost wireless networks (partly or fully formed or ad-hoc) and provide a ‘connected environment’ where individuals, vehicles and infrastructure can co-exist and cooperate, thus delivering more knowledge about the transport environment, the state of the network and who indeed is traveling or wishes to travel. This may offer benefits in terms of real-time management, optimization of transport systems, intelligent design and the use of such systems for innovative road charging and possibly carbon trading schemes as well as through the Cooperative Vehicle and Highway Systems for safety and control applications. See the research and potential use of a Mote based wireless sensor network in the video below:


Initial studies suggest vehicle to vehicle, vehicle to infrastructure, and infrastructure to infrastructure communication and in-vehicle monitoring and environmental monitoring may exist for Motes in the transport domain. Over the last few years, many different versions of 'smartdust devices' have been designed and built by various companies and institutions. Such devices can be used to sense a wide range of environmental parameters as well as vehicle speed, vehicle direction and vehicle presence in the infrastructure. Even though there are several platforms available on the market, Newcastle University chose Crossbow's MICA family motes for the EMMA and TRACKSS projects due to its commercial success in many wireless sensor network applications. Also, Newcastle University has successfully used MICA family motes in its other research projects such as the ASTRA project. Low power wireless communication and low power sensing capabilities are essential for sensor network applications which are supported by Crossbow's Mote family.

Itsdiagram_3 The ASTRA project investigated the use of mobile ad-hoc networks, and more specifically, Motes for transport applications. The project examined the current state-of-the-art using MICA motes. A trial using Motes technology was hosted in Newcastle with a pervasive intelligent corridor established by a network of fixed Motes on roads near Newcastle Central Station. Mobile Motes were also placed in several buses. Communication between a static node and a moving node on-board a vehicle was achieved, showing that communication can take place between road side and vehicles using a network of Motes. Evaluation of the system revealed that the main limitation of the technology at the present time is battery life. The experiments have demonstrated that Motes can be used for communication between a fixed infrastructure and a moving vehicle up to a speed of 50 mph. Further testing of the devices at higher speeds (60 and 70 mph) to asses the suitability of smartdust/Motes in applications alongside fast moving roads such as a motorway will be conducted.

Itstrackss_3The focus of the EU funded TRACKSS project is to research advanced communications concepts, open inter operable and scalable system architectures that allow easy upgrading, advanced sensor infrastructure, dependable software, robust positioning technologies and their integration into intelligent co-operative systems to support a range of core functions in the areas of road and vehicle safety and traffic management and control. The overall aim is to develop new systems for cooperative sensing and predict flows, infrastructure and environmental conditions surrounding traffic, with a view to improving road transport safety and efficiency. To support the demonstration phase of the project, Newcastle University will develop a new technology for ‘smart’ detection on vehicles and infrastructure and a common framework for data collection and access from the entire array of sensors being deployed and tested in the TRACKSS project.

ItsarchitectureIn the case of EMMA, the focus is automobiles and their constituent parts, and the infrastructure they utilize (both physical in the sense of roads and the ICT embedded in them for monitoring and control purposes). If we think more widely at present, most of the world’s computing power is already embedded invisibly into the things around us. The personal computers, music players and other gadgets are just the tip of the iceberg. They probably represent no more than 1% of the computing power we have deployed around us. A typical car today will have at least 20 microprocessors and a host of other electronics contributing to the general functionality required by a modern car as well as the ‘value added services’ which may be the unique selling point of a particular vehicle – whether the application, be: better information on how the vehicle is running; safety applications; or infotainment in the vehicle. The Embedded Middleware in Mobility Applications project (EMMA) application domain of transport will be taken as a pilot example where EMMA will foster cost-efficient ambient intelligence systems with optimal performance, high confidence and faster deployment. The MICAz Mote will be the best suitable platform for the EMMA project since it features sensing and networking capabilities with low power consumption.

The EMMA and TRACKSS projects being pursued by Newcastle University are committed to play a major role in creating new possibilities in the future ITS by using Mote technology. For more information on Crossbow's Mote platforms, contact sales or visit our website.

April 07, 2008

Programming with the .Builder Jedi

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

Martinturon_2 Today we are lucky enough to have a quick lesson in programming using Crossbow's Imote2.Builder kit with Crossbow's Director of Wireless Software and our resident Programming Jedi - Martin Turon. 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.

Builderimote2 Embedded programming has historically involved pulling out your C compile, and writing detailed code that needs to carefully manage limited memory resources, hardware interrupts, and low-level bus interfaces. The benefits brought by newer high-level languages such as Java and C# have lagged entry into this space by about 5-10 years. The delay can be attributed to limits to memory and processing speeds within the space due to aggressive cost requirements, and a lack of tools and software support for embedded platforms compared to PC/server platforms. More recently, the J2ME and Windows CE platforms have made Java and C# respectively available to smart phone and PDA application developers. But both of these environments continue to limit the amount of control the developer has over low-level interrupts and hardware resources. The Microsoft .NET Micro environment that powers the Crossbow Imote2 .Builder kit allows programming an embedded device with C#, while providing native control over hardware resources such as the I2C, SPI, and UART buses. Programming a native IEEE 802.15.4 radio driver for example is possible in this environment, and the full source code for such an implementation is provided.

There are it seems endless sites that compare C# against Java. They can in many ways be considered the same language fundamentally, with C# being largely derived from its Java parent adding a few new keywords and constructs. The biggest difference comes with the libraries that the developer links to, which can be very different depending on the version and underlying platform that is chosen. C# does have one essential advantage however for the embedded developer, and that is native handling of unsigned types. In Java all variables are signed, so in order to manipulate a 16-bit ADC value for example one needs to jump through hoops:

// Java code to read little-endian unsigned 16-bit data: complex.
public short readShortLE() throws IOException {
     int w, wlo, whi;
     wlo = (0x000000FF & (int)super.readByte());
     whi = (0x000000FF & (int)super.readByte()) << 8;
     w = whi | wlo;
     return (short)w;
}

// C# code to read little-endian unsigned 16-bit data: simple.
public ushort readShortLE() {
    return super.readByte() | (super.readByte() << 8);
}

This example shows how much more intuitive and readable the embedded C# code is over Java due to the simple addition of ushort and uint types. Writing Java code that correctly handles unsigned types is actually fairly difficult to do, and the initial implementation of such code tends to have hidden bugs for certain portions of the data range.

In the following example, the start of a simple SPI-based sensor driver is presented. This driver sends low level commands over a SPI bus to a digital 3-axis accelerometer. The code shows how to initiate a SPI configuration and how to read and write registers over the bus. The example also includes creating a property API for accessing a particular register on the digital sensor, in this case Accel_X. On node conversions and intelligence can be easily written right into the driver within the property code block.

using System;
using Microsoft.SPOT;
using Microsoft.SPOT.Hardware;
using Crossbow.platform.imote2;     
   
public
class AccelerometerSensor
{
    internal enum Reg : byte
    {
       // Control
       CTRL_REG1 = 0x20,
       CTRL_REG2 = 0x21,
       // Accelerometer Data Registers
       OUTX_L = 0x28,
       OUTX_H = 0x29,
       // ...More commands left out for brevity..
    }; 
        SPI                _spi;

    SPI
.Configuration _spiConfig;
       

    public void Initialize(SPI.Configuration spiConfig)
    {
       if (spiConfig == null)
       {
          _spiConfig = new SPI.Configuration(
              Pins.GPIO_PORT_SSP1_SFRM, // Chip select port
              false
, // Chip select active state
              20, // Chip select setup time
              20, // Chip select hold time
              false
, // Clock idle state
              true
// Clock edge
              986, // Clock rate KHz (986KHz)
              SPI
.SPI_module.SPI1 // SPI port connected to LIS3L02DQ
              );
       }
       else
       {
          _spiConfig = spiConfig;
       }
       try
       {
          _spi = new SPI(_spiConfig);
       }
       catch (Exception ee)
       {
          Debug.Print(ee.ToString()); 
       }
            
       // Power up, no decimation, no self-test, enable x/y/z

       WriteReg(Reg.CTRL_REG1, 0xC7);
       WriteReg(Reg.CTRL_REG2, 0x04);
       // Normal, continuous, little-endian, 4-wire,
       // right justified, enable data-ready
       
    } 


    internal
void WriteReg(Reg reg, ushort data)
    {
       byte[] write = new byte[] { (byte)reg, (byte)(data) };
       try {
          _spi.Write(write);
       }
       catch (Exception ee)
       {
          Debug.Print(ee.ToString());
       }
    }

    internal byte ReadReg(Reg reg)
    {
       byte[] read  = new byte[1];
       byte[] write = new byte[] { (byte)((int)reg | 0x80), 0 };
       try
       {
          _spi.WriteRead(write, read, 1);
       }
       catch (Exception ee)
       {
         Debug.Print(ee.ToString());
       }
       return read[0];
    } 

   
    public
ushort Accel_X
    {
       get {
          ushort accel_x = (ushort)ReadReg(Reg.OUTX_H) <<8;   
          accel_x |= (ushort)ReadReg(Reg.OUTX_L);
          return accel_x;
       }
    }
}

Moteplatformcomparison_7

The Imote2 hardware provides arguably the highest performance mote platform while retaining good power characteristics, dwarfing other platforms with regard to processing speed, and memory and flash size (click on the table for more details).

This capable hardware coupled with the simple C# environment of Visual Studio and the Microsoft .NET Micro framework results in higher developer productivity. Creating advanced WSN (wireless sensor network) applications and test beds that involve in-network processing, high-speed sampling, and logging has never been easier.

Buildermotepong The kit CD includes full C# source code for an Imote2 application that streams accelerometer data over the IEEE 802.15.4 radio. Two sample PC applications receive this data from an Imote2 base station and interpret it: MotePlot and MotePong. The former plots the real-time accelerometer data as a chart, and the later interprets the readings as control information to play a simple and familiar game.

To purchase a WSN-IMOTE2.Builder kit or receive further information on this platform, feel free to email Crossbow directly at sales@xbow.com. Additional details such as datasheets, manuals are available here on Crossbow's website. The Imote2.Builder simplifies and accelerates the design of wireless sensor applications. The level of performance and capability that the Imote2 brings to wireless sensor networks breaks the computational and memory limitations of current platforms by orders of magnitude for applications involving data-rich computations where there is a need for both high performance and high bandwidth.

March 14, 2008

Heated up about your Energy Bill? Motes to the Rescue!

by Ralph Kling, Chief Architect, Crossbow Technology, Inc.

DigitalthermostatWhen I opened up my Gas and Electric bill from our local utility (PG&E here in Northern California) I was shocked: it was over $100 more than I expected. Some further checking confirmed that it was also substantially higher than during the same time last year, by about that amount. So what happened? Sure, energy prices likely have increased since last year and maybe it was a bit colder here the last month. But it still didn’t add up… 

A few years ago I had installed a shiny new programmable digital thermostat to replace an old mechanical gizmo that must have been around since the house was built in the 1950s. And I actually did read the manual (well, some of it anyways) and spent quite a bit of time programming it with different cycles for days and nights and weekends and so on. And, though I was very proud of that accomplishment at the time, the promised energy savings that I had hoped for actually never really materialized. My utility bills stayed pretty much the same before and after the thermostat installation. 

Netbridge_start_kit_4 So after receiving the last bill, I investigated the thermostat but it seemed to work just fine and none of the settings had been changed since I had originally made them. After contemplating this for a bit, I had an idea. Since I am working at Crossbow, the leader in Wireless Sensing technologies I have access to the essential tools needed to solve this mystery: I took home a Wireless Sensor Starter Kit which consists of two sensor nodes and a base station. I also added a Stargate Netbridge gateway to record and visualize the data from the sensor nodes without the need to install anything on my PC (or even have it running and consuming energy during the measurements). 

The next day, I installed the “Sensor Network” in my house. It was really simple, just plug the gateway into my home router, place the sensor nodes in the rooms I wanted to monitor and turn them on. All networking and data gathering happen automatically from then on. The sensor nodes are battery powered thus they can be placed pretty much anywhere eliminating the search for outlets. I placed the nodes in the kids’ rooms in the center and back of the house. The nodes have all sorts of sensors including light, humidity, atmospheric pressure etc. but I was particularly interested in the temperature readings. 

The next day, I pulled up the temperature profile gathered so far and I was shocked:

Moteexplorertempprofile_3

 

The Thermostat was set to 70°F, (about 21°C) during the morning and evening and to 65°F, (18°C) during the rest of the day and at night. The measurements show an average of 24°C during the high and 20°C during the low period, a full 2-3 degrees more than expected. Why were the readings so far off from the preset values at the thermostat? And then it dawned on me: the thermostat was in the living room at the other end of the house. And I remembered that last year in the summer I had closed the heating air vents in that room to prevent the kids’ Lego pieces from falling into the shafts (they are really hard to retrieve from there). Of course I had forgotten about that in the fall when I restarted the heater. 

So the warm air had to make its way through half the house before the living room (and the thermostat) warmed up thus raising the temperature in the other rooms way to high (as well as the heating bill). But this was still a theory that needed to be proven. So I opened up the living room vents all the way, let the sensors collect more data and waited impatiently for the data from the next day. The results were nothing short of astounding:

Moteexplorertempprofile2

 

The temperatures in the kids’ rooms now averaged 21°C during the high and 19°C during the low periods, much closer to the desired thermostat settings. Beyond that, the data also shows that one room warms up more poorly than the other (it has more outside walls with poor insulation). Adjusting the vents in those rooms as well could further equalize the temperature readings. 

So, with a very simple setup (it took literally 5 minutes to set up) I was able to gather a lot of valuable data and hopefully significantly reduce my heating bill (we will see next month). And all that without lowering the temperature settings on the thermostat! Beyond that there are lots more insights that can be gathered from the data: The slope between the day and night settings shows how fast the house cools down and how well it is insulated. Other data like the humidity measurements can point to potential problems with mold if the readings are too high. Do your kids always “forget” to turn off the lights – well a plot of the data can very convincingly show how much energy is wasted. 

I am sure there are lots more good ideas – reader’s suggestions are welcome!

Ralphkling_2 Dr. Ralph Kling is currently the Chief Architect of the Wireless Business Unit at Crossbow Technology. Ralph is leading the Wireless engineering team at Crossbow and is responsible for new product strategies, technical directions and Standards activities.

Previously, Ralph was Principal Architect and Director of Sensor Network Operation at Intel Corporate Research. In this capacity, he was responsible for ground-breaking research in the area of Wireless Sensor Network Platforms that resulted in such novel designs as the Intel Mote and Imote2. Before joining Intel Research, Ralph's previous assignments include managing the Itanium
®
Processor Family microarchitecture/ performance group and the Microprocessor Research Lab (MRL).

Ralph obtained his Master's and Ph.D. degrees from the University of Illinois at Urbana-Champaign. His thesis research focused on Simulated Evolution, a new global optimization method for integrated circuit designs. Prior to coming to the US on a Fulbright scholarship, Ralph studied Electrical Engineering in his hometown of Hanover in Germany. He enjoys skiing in the winter and beaches in the summer.

February 27, 2008

TinyOS 2.0 Support for Crossbow's IRIS Mote Platform

Tinyos_3 Earlier this month, Crossbow announced the availability of the TinyOS 2.0 Operating System for Crossbow's advanced IRIS Motes. TinyOS 2.0 is the latest major release of the popular open source embedded operating system. This release now enables developers to use the latest generation TinyOS software on the latest generation Sensor Network hardware. TinyOS is an open-source operating system designed for wireless embedded sensor networks. It features a component-based architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. The TinyOS component library includes network protocols, distributed services, sensor drivers, and data acquisition tools. The event-driven execution model enables fine-grained power management yet allows the scheduling flexibility made necessary by the unpredictable nature of wireless communication and physical world interfaces.

Irismote_4 The IRIS Mote platform offers users excellent RF range (over 3x improved radio range of previous generation motes), substantially lower sleep current (50% of previous generations), and double the program memory (8KB). In conjunction with TinyOS 2.0, users now have a better hardware abstraction model, improved timers, sensor interfaces, power management, arbitration and much more. The IRIS Mote is a 2.4 GHz module used for enabling low-power wireless sensor networks and is also supported by Crossbow's MoteWorks software development environment based on open-source TinyOS.

Vanderbilt_2 The IRIS port was created by Vanderbilt University's Institute for Software Integrated Systems. Wireless sensor network developers and researchers benefit from Vanderbilt Universitys well-established, recognized expertise in TinyOS development and continued support of released code. We are pleased to have received Crossbows support for the advancement of the open source TinyOS 2.0 environment and continued development of leading edge wireless sensor network platforms, said Professor Akos Ledeczi, Research Associate Professor and principal investigator on the project.

Crossbow is the leading provider of wireless sensor nodes, or motes, and continues to offer broad operating system choice. One or more Crossbow platforms are now supported on development environments including Contiki, Linux, LiteOS, Mantis, Microsoft .NET Micro Framework, MoteWorks, SOS, and TinyOS. Crossbows ability to provide software choice enables rapid development of the newest and most innovative applications of wireless sensor networks, said Robert Robinson, Vice President of Sales & Marketing for Crossbows wireless division.    

Instructions for download and use of Vanderbilts open-source code release of TinyOS 2.0 for IRIS are available here. Crossbows IRIS product line includes the IRIS OEM Module, IRIS Mote, and related sensor boards. Check out the IRIS platform and other Crossbow wireless sensor networking products here.

February 20, 2008

Swarm Navigation with TelosB Motes

Easysenswarmagent_3 The concept introduced below provides a highly effective swarm navigation scheme that is of low complexity, robust, and highly scalable. Rather than using a few highly complex and expensive swarm agents to complete a mission, the group at Easysen believe in the advantages of large ultra-low complexity swarms that solve problems reliably through emergent behavior.

EasySen is a start-up company who in conjunction with the University of Notre Dame’s Mobile Sensing Systems (MOSES) Lab, has developed an autonomous sensor swarm that uses TelosB nodes for navigation of swarm agents. The principle is based on Zigbee radio beacon induced potential fields and provides an ultra low cost and complexity solution to mobile sensing for land, sea, and air vehicles. Stereo TelosB receivers and stereo sensor suites (EasySen SBT80 / SBT30-EDU) allow for rather elaborate task execution.

Easysentelosb_6 The group at EasySen proposed the use of radio frequency beacons to generate (switched) potential fields for navigation of large numbers of swarm agents. The idea is to use attractive beacons as waypoints and local attractors. Repelling beacons on each agent and waypoint are used to control the density of agents and avoid collisions.  If a certain sequence of waypoints defines a navigation path, then the attractive beacons need to be distinguishable and need to be visited in a certain sequence. (This is easily implemented in form of a finite state machine in each sensor swarm agent.) Repelling beacons are local and do not need to be distinguishable.  Individual sensor swarm agents are equipped with a side-looking stereo receiver with opposite directions of highest sensitivity. A simple difference between the left and the right Receive Signal Strength Intensity (RSSI) allows the agent to detect in which half space (relative to the center length axis of the vehicle) a beacon is located. One can then navigate towards a beacon by always moving towards the receiver side that has produced the stronger RSSI reading. For repelling beacons, one always moves towards the direction of the smaller RSSI signal.

The applications of this paradigm are many and range from environmental clean-up such as oil spill removal to surveillance and protection tasks. A ground vehicle swarm that performs a simple detection task is shown in the video below:

The company also produces readily usable plug-in surveillance sensor suites for the TelosB wireless 802.15.4 platform:

  • The Wi-Eye, an ultra-sensitive sensor board that is capable of detecting the IR signature of moving vehicles from as far as hundreds of feet away.
  • The SBT80 is an 8-modality sensing platform, ideal for sensor fusion applications.

Both sensor suites (the Wi-Eye and the SBT80) are prime candidates for perimeter security, traffic monitoring, tracking, and occupancy detection tasks, just to name a few. In addition, EasySen also offers SBT30-EDU, a low-price educational prototyping board that interfaces to external signal sources.  Click here for more information on the TelosB Mote platform.

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