distributed sensor networks with collective...

UNCLASSIFIED LA-UR-06-70871

Distributed Sensor Networks with Collective Computation


ISR-3 CCS-5Angela M. Mielke - Project Lead Matthew S. NassrSean M. Brennan Stephan J. EidenbenzJanette R. FrigoHarvey Hirst P-21Diana Jackson Eric Y. RabyJesus JacquezMark C. Smith Arizona State UniversityDavid Valencia Charles Colbourn

Violet SyrotiukT-10David C. Torney University of New Mexico

Arthur B. MaccabeEES-11 James HoreyJames T. RutledgeDouglas M. Alde

N-1Ernst I. Esch

Project Team


What are DSN’s and why do we care?

• DSN’s are self-organizing networks of sensors with computation and communication capabilities.

• DSN’s are ideally designed for use in application areas such as:

Remote sensingPersistent surveillanceCovert operations

Event


The Classical DSN approach employs a Star Topology.

Advantages:• Simple network architecture• Raw data available at the Central Processing Station

Disadvantages:• Catastrophic single-point failures• Potential long-range communication• Large quantities of transmitted data• Difficult network scale-up

Central Processing StationEvent


Collective Computation is an alternative network architecture providing in-situ data processing.

Advantages:• Network size scalable• Fault-tolerance• Conclusions to user node(s)

End-user(s) receivingtimely simple conclusion

Sensors inter-communicatefor collective computation

Disadvantages:• Complicated node design• Complicated network design


Network development begins with simulation.

• The Distributed Sensors Simulator (DSS) was developed due to shortcomings in other commonly available sensor network simulators.

• Goal was to provide the infrastructure for environmental simulations and wireless network simulations.

• Developed in conjunction with the University of New Mexico.

• Received DOE approval for Open Source Distribution on April 14, 2004.


Let’s step through a simulation example…

• Sensor node placement is highlighted here.


An example continued…

• Acoustic node #26 makes a detection.



• Node #7 makes the radiation detection.



• The alert is exfiltrated to the user.


Simulator Developments - DSS to N-sim• Improved framework for environmental

phenomenology – DSS included basic information about source

propagation and sensor detections.– N-sim will include physical dynamics and obstacle

models allowing for direct, reflected and refracted propagation paths as applicable.

• Improved wireless simulation characteristics– DSS relied on simple transmission models.– N-sim will utilize probabilistic models that include

obstructions and collisions.• Code developed in N-sim will simply require a

compiler change for a port to hardware implementation.


Commercial Wireless Sensor Networks• Mesh Network• Self-organizing• Self-healing• Encryption• Spread Spectrum• Multi-hopping• Typically Star Topology• Provide Sensor Network

Backbone• Manufactures include:

DUST, Crossbow, Ember, Millenial, Moteiv, …

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.


The Sponsor Challenge - Application Development

• Demonstrate simulation results• Evaluate commercial hardware• Investigate two alternative wireless network

approachesMote approach - small, low power devices, advanced wireless capabilities, minimal data processing capabilities, small operating system (TinyOS)PDA-sized device approach - moderate-sized and moderate-powered devices, 802.11b wireless capability, significant data processing capabilities, general purpose operating system (Linux)


Target Application - Roadway Monitoring for Radioactive Materials

• Radioactive source detection• Focus of this application is on the

mitigation of the potential for terrorist usage of RDDs.

• Mote network detects vehicle presence.

• Motes queue the PDA network to begin coherent addition techniques for material detection.

• Radiation background updates and data evaluation occur on the PDAs.

• Source detection is exfiltrated to a command-and-control console.


Heterogeneous Vehicle Detection Mote Network

• Mica2 Crossbow motes were deployed along a roadway for vehicle detection

• On-board MTS310 multi-sensor boards utilized

• Self-organizing network• Magnetometers used for vehicle

detection• PDA network utilized the Linux-based

Sharp Zaurus SL-5600.• UNM delivered software for multi-hop

routing on the PDAs• The PDA network connects to the

radiation detectors as well as to the Crossbow gateways

• Radiation detectors are COTS serial Geiger-Mueller tubes with a 1.75 inch active window and a length of 0.5 inches.


Challenges Encountered in Initial Field Experiments

• Network Timing• Accelerometer Insensitivity• RS232 Non-compliance of the PDA

Devices• Mote Failures• Inconsistent Sensor Readings• Transmission Range Inconsistencies• Battery Lifetime• PDA Power Consumption• Magnetometer degradation


Current Research Areas• Sensor characterization and exploitation through

testbed development• Transmission protocol characterization and

refinement• Algorithm development for event detection,

classification and tracking• Power consumption and management studies• Network scale-up• Network and device security (ie. spoofing and

tamper resistance)• Node programming and tasking• Improved radiation detection techniques• Hardware benchmarking


Routing Protocol StudiesMotivation• Using standard routing sensor networks are said to be impractical and

unreliable with traffic beyond 5-6 hops.• MINTRoute (the TinyOS standard) delivers 50% of packets at 4 packets per

second (pps) and 90% at 1 pps with 8-10 hops under indoor conditions.• CCS-5 developed several Parametric Probabilistic protocols for such

applications.– Directed Transmission and Destination Attractor

• Promising simulation results.


Protocol Validation Experiments



• Experimental systems varied in size from 15 to 30 Mica2 nodes

– Atmel ATmega128L low-power microcontroller running TinyOS, 4k of RAM, 128k of Flash

– 8 MHz processor– Chipcon CC1000 radio, 915 MHz, 19.2 Kbps, – Maximum range ~ 400 feet

• Sources inject 1 packet per second• Experiments run with 1, 3, 5, 7, 9 sources• Grid and random topologies utilized• A network of 4 to 8 Crossbow Stargate devices

are used for passive network monitoring– 400 MHz Xscale processor, running Linux, powered by

4200 mAh NiMH rechargeable batteries– Communication through 802.11b CF cards– Packet forwarding to an in-network Tablet PC


Experimental Results

• In outdoor experimental testing Directed Transmission performs significantly better than MINTroute– 25-60% better under low load conditions– 5-10% better under high load conditions

• Field experimentation has validated the protocol development simulation studies


Node Benchmarking

• Event detection networks require more computation power within each node.

• New products are smaller, more energy efficient and more capable than the traditional WSN nodes.

• Just how much info can be processed on such a device?• What are the tradeoffs in space, energy, efficiency

between the more capable devices and the Mica2 series of mote devices?




Device Comparison• Apple PowerBook G4, OS X 10.4.7

– 500 MHz PowerPC G4– 256 MB RAM– 18 GB Hard Drive

• Gumstix, Linux 2.6.17– 400 MHz Intel Xscale, No FPU– 16 MB Flash File System– 64 MB RAM

• Stargate, Linux 2.4.19– 400 MHz Intel, XSCale, No FPU– 32 MB Flash File System– 64 MB RAM


Benchmarking Results• Nbench is the Linux/Unix version

of the ByteMark benchmarks from byte.com

• Tests the performance of various CPU and FPU abilities

• Tests:– Numeric Sort, String Sort, Bitfield– Emulated Floating-Point– Fourier Coefficients, Assignment– IDEA Encryption, Huffman Encoding– Neural Network– Lower Upper Decomposition

• Discrepancy due to compiler differences in the floating-point emulation packages


Node Programming and Tasking

Motivation:• Every application requires domain specialists as well as an army of

computer scientists and engineers to set up the WSN hardware.• Significant issues revolve around:

– Node redundancy– Communication reliability– Sensor inaccuracy– Node programmability

• UNM is developing an architecture for programming and tasking a WSN called Kensho.– Programming involves writing the functions to be distributed throughout

the network.– Tasking involves distributing the set of functions onto the application

nodes.


Kensho - Ease in Network Programmability• Kensho is a lightweight communications and tasking library.• Uses simple group-based methods for tasking.• Users are able to associate groups with sets of functions.• The library is divided into two sub-libraries:

– The Collective library handles tasking the entire sensor network.– The Node library handles communication within groups.


Research Areas for Future Investigation

• Long-range data exfiltration• Expansion of university and industrial collaborations

– Arizona State University– The University of New Mexico– New Mexico State University– Boston University

• Expansion of application tools and sponsorsMethods of energy harvesting for long-term deploymentsSystem-wide power managementIncorporation of knowledge discovery techniques for data management


Unique Project StrengthsAbility to initially design and evaluate systems in simulation Development of persistent surveillance applications utilizing COTS technology

Implementation of networks utilizing complicated sensors

Collective ComputationMoving from Data Collection to Event Detection


More Information?

www.lanl.gov/dsnAngela Mielke(505) [email protected]

http://www.lanl.gov/dsn

mailto:[email protected]

distributed sensor networks with collective...

Documents