Cognitive Network Discovery and Characterization

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$99,628.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-11-M-1132
Award Id:
n/a
Agency Tracking Number:
F103-168-1895
Solicitation Year:
2010
Solicitation Topic Code:
AF103-168
Solicitation Number:
2010.3
Small Business Information
1925 Isaac Newton Sq E, Suite 100, Reston, VA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
129152422
Principal Investigator:
Jay Livingston
Principal Engineer
(703) 390-9315
jlivingston@sdsi.net
Business Contact:
Hank Orejuela
CEO
(703) 390-9315
horejuela@sdsi.net
Research Institute:
Stub




Abstract
Our system uses the Cognitive Radio itself as a sensor to form a Cognitive Network that performs non-cooperative hidden node detection and characterization. PHY layer information is collected in order to perform Specific Emitter Identification (SEI), Geolocation, and Network Topology Discovery, making it possible to detect a hidden node and hidden node candidates. Data-link layer (DLL) information is also shared amongst nodes in the network, providing the ability to characterize the hidden node"s behavior and complete the detection process. We assume that the non-cooperative hidden node has the ability to masquerade to some degree as a network node. We will develop models of cognitive radios and networks, simulating the distributed algorithms to be used in hidden node discovery. Collected RF waveforms from a cognitive radio will be used for SEI and Geolocation study. Other simulation tools will characterize the network at the Data-link layer. Algorithms will be simulated and the results compared to determine the best approach to hidden node detection and characterization. We are teamed with a cognitive radio provider, Shared Spectrum Company, in order to bring greater fidelity to our modeling and simulation. BENEFIT: Our system provides the ability for the network to become self aware. Such a network can alter its behavior to accomplish goals specified by the network designers. For example, a self aware network can balance network loading and optimize network routing, creating a more efficient network. The network has the ability to change its transmission patterns, frequencies, and bandwidth, to both avoid interfering with a hidden node and the node interfering with the network, yielding higher network throughput and reliability. It can prevent network abusers from using the system for their own purposes by denying them access to the network, again, yielding higher network availability and reliability. The system can be directly applied to both commercial and DoD applications, since both entities desire better network operation. Commercial firms will be more interested in the ability to operate efficiently, deny access to network abusers, and avoid interference with hidden nodes. The DoD will be more interested in the ability to detect and characterize hidden nodes that have nefarious intent.

* information listed above is at the time of submission.

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