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Multi-Level Detection and Characterization Mechanisms against the Intentional…

Award Information

Agency:
Department of Defense
Branch:
N/A
Award ID:
Program Year/Program:
2011 / SBIR
Agency Tracking Number:
F103-168-2470
Solicitation Year:
2010
Solicitation Topic Code:
AF103-168
Solicitation Number:
2010.3
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive Suite 400 Rockville, MD 20855-
View profile »
Woman-Owned: Yes
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2011
Title: Multi-Level Detection and Characterization Mechanisms against the Intentional Hidden Node Problem
Agency: DOD
Contract: FA8650-11-M-1129
Award Amount: $100,000.00
 

Abstract:

In this proposal, Intelligent Automation Inc. (IAI), along with Professor Jung-Min Park (Virginia Tech), propose to develop a Multi-Level Detection and Characterization (MuLDaC) approach to defense against the intentional hidden node problem in cognitive radio networks (CRNs). The proposed MuLDaC approach utilizes multiple disciplines including PHY layer detection, authentication, and trust-aware data fusion. Most existing research focuses on the unintentional hidden node problem, and hence cannot be directly applied to the intentional hidden node problem. In addition, due to high flexibility and rapid reconfiguration capability of cognitive radio, it is much easier for a node to intentionally"hide"in the cognitive radio environment, compared with the traditional networks. Moreover, to discover the unintentional hidden nodes, distributed cooperative spectrum sensing schemes are proposed; however, they give even more opportunities to the intentional hidden nodes. Thus, a complete study is necessary and yet not available for characterizing various behaviors of intentional hidden nodes in CRNs, their impacts on the network performance, and the corresponding solutions. The objective of the proposed MuLDaC approach is to properly and promptly detect the selfish and/or malicious nodes and determine their intents, based on the established knowledge of the intentional hidden nodes in tactical networks. BENEFIT: The proposed detection and characterization approach has tremendous potential to greatly enhanced survivability for the war-fighters. Given the GIG vision, such heterogeneous and dynamic wireless networks will be common, and the developed multi-level mechanisms can be applied to various military networks potentially supporting a number of major programs such as Airborne Networks Program, Joint Strike Fighter (JSF), Future Combat System (FCS), etc. The commercial drive for reliable communication is also increasing due to the increasing popularity of wireless network technologies. The potential commercial applications include commercial cognitive radio networks, wireless sensor networks, wireless ad hoc networks, and wireless mesh networks where there exist both the infrastructure and the more mobile"edge"networks. The size of the market is quite large and may grow rapidly with the commercial demand for network reliability and availability.

Principal Investigator:

Justin Yackoski
Research Scientist
(301) 294-4251
jyackoski@i-a-i.com

Business Contact:

Mark James
Director, Contracts and Proposals
(301) 294-5221
mjames@i-a-i.com
Small Business Information at Submission:

Intelligent Automation, Inc.
15400 Calhoun Drive Suite 400 Rockville, MD -

EIN/Tax ID: 521497192
DUNS: N/A
Number of Employees:
Woman-Owned: Yes
Minority-Owned: No
HUBZone-Owned: No