distributed sensor networks - boston university · distributed sensor networks angela m. mielke1,...

UNCLASSIFIED LA-UR-06-1509

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Distributed Sensor Networks

Angela M. Mielke1, Sean M. Brennan1, Mark C. Smith1, Janette R. Frigo1, Diana Jackson1, David C. Torney2, James T. Rutledge3, Douglas M. Alde3,

Matthew S. Nassar4, Stephan J. Eidenbenz4, James Horey5, Arthur B. Maccabe5, Violet Syrotiuk6, Charles Colbourn6

1ISR-3 - Space Data Systems, 2T-10 - Theoretical Biology and Biophysics,3EES-11 - Geophysics, 4CCS-5 - Discrete Simulations Sciences,

5The University of New Mexico,6Arizona State University


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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


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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


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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


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The Distributed Sensors Simulator is a simulation infrastructure for network prototyping.

• Developed due to shortcomings in other commonly available sensor network simulators.

• Provides the infrastructure for environmental simulations and wireless network simulations.

• Developed in conjunction with the University of New Mexico.

• DOE approval for Open Source Distribution of the Distributed Sensors Simulator (DSS), Version 0.8 - was obtained on April 14, 2004.


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Let’s step through a simulation example…

• Sensor node placement is highlighted here.


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An example continued…

• Acoustic node #26 makes a detection.


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• Node #7 makes the radiation detection.


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• The alert is exfiltrated to the user.


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• The vehicle passes out of the network.


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• A regional failure is simulated.


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Simulator Developments - DSS to NSIM • Improved framework for environmental

phenomenology – DSS included basic information about source

propagation and sensor detections.

– NSIM 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.

– NSIM will utilize probabilistic models that include obstructions and collisions.

• NSIM is an Emulation engine enabling simple compiler changes for code ports from simulator to actual hardware.


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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

Crossbow Mica2/Mica2dot

Millennial Net I-bean

Ember Modules


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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 approach - moderate-sized and moderate-powered devices, 802.11b wireless capability, significant data processing capabilities, general purpose operating system (Linux)


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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.


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Vehicle Detection Mote Network

• Deployed network of Mica2 Crossbow motes

• Deployed along a roadway

• On-board sensors on the Crossbow MTS310 multi-sensor board included:

2-axis accelerometer

2-axis magnetometer

Light, temperature

Microphone, sounder

• Self-organizing

• Multi-hop capability

• Magnetometers used for vehicle detection


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PDA Network

• PDA network utilized the Linux-based Sharp Zaurus SL-5600.

• In field testing -Range limit is approximately 450’UNM delivered software for multi-hop routing on the PDAs.

• PDA network connects to the radiation detectors as well as to the Crossbow gateways thus bridging the two networks.

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


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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


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Research Areas under Current Investigation

• Sensor characterization and exploitation through testbed development

• Transmission protocol characterization and refinement

• Algorithm development for event detection– Vehicle classification using geophone data

– Hyperspectral Imaging

• Power consumption and management studies

• Network scale-up

• Network and device security (ie. spoofing and tamper resistance)

• Improved radiation detection techniques


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Transmission Protocol Characterization Studies

• Development of a non-invasive monitoring system utilizing the Crossbow Stargates

• Parametric Routing Protocols developed in CCS-5 (Destination-Attractor and Directed-Transmission) are being evaluated against simulation results.

• Additional protocols will be tested– WSN, DSDV, AODV

• Protocols are implemented in TinyOSon the Crossbow Mica2 motes

• Current efforts focus on validation of the experimental results

– Collisions, Network Stability, Timing, Reception/Transmission


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Power Consumption/Energy Harvesting

• Battery lifetime studies for different battery types and different operational conditions

• RF power versus transmission distance studies

• Solar recharge capabilities

• Outfitting the Crossbow Stargates for an off-grid configuration


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Research Areas for Future Investigation• Long-range data exfiltration

• Expansion of university and industrial collaborations

• Expansion of application tools and sponsors

Air surveillance

Border security

Event monitoring

Internal security perimeters

Methods of energy harvesting for long-term deployments

System-wide power management

Incorporation of knowledge discovery techniques for data management


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Project Strengths

Ability 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


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More Information?

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

distributed sensor networks - boston university · distributed sensor networks angela m. mielke1,...

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