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Network Coding and Network Tomography (NCNT) Analysis and Algorithms for…

Award Information

Department of Defense
Air Force
Award ID:
Program Year/Program:
2010 / STTR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
AF 09TT15
Solicitation Number:
Small Business Information
InfoBeyond Technology LLC
Suite 220 10400 Linn Station Rd. Louisville, KY -
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Phase 1
Fiscal Year: 2010
Title: Network Coding and Network Tomography (NCNT) Analysis and Algorithms for Dynamic Airborne Networks
Agency / Branch: DOD / USAF
Contract: FA9550-10-C-0153
Award Amount: $99,837.00


The airborne network suffers from the limitations of highly constrained network capacity due to wireless link communication and intermittent connectivity among platforms. Information coding theory is a very new technology that is initially proposed for computer networks in 2001 and for ad hoc networks in 2006. The recent study shows it is able to increase the network capacity for mobile network to 10% or more. In this proposal, we propose the Network Coding and Network Tomography (NCNT) analysis and algorithms for dynamic airborne networks. NCNT includes three algorithms namely, Linear Programing-based Multicast Coding (LPMC), Opportunistic Unicast Coding (OUC), and Pseudo-log Likelihood Estimation (PLE) algorithms. NCNT formulates the network coding problem as an optimization problem. LPMC and OUC are multicast coding algorithm and unicast coding algorithm respectively that increases the airborne network capacity. The time and space complexities of them show the scalability and adaptability for airborne networks. PLE uses network tomography for dynamic network analysis. Our primary study in this proposal shows our proposed algorithms can increase the network capacity superior to other existing approaches and can be adapted to airborne network for practical usage without the need of hardware upgrading. BENEFIT: Air Force can gain significant value from the commercialized dual-use products of the proposed NCNT technology. The developed coding algorithms increase the network capacity (e.g., >10%) for airborne networks without the need of any new hardware. This is critical for Air Force due to the constraint of wireless bandwidth in the airborne network. The resulting profit for Air Force comes from the developed algorithms and protocols that can be used to upgrade a variety of airborne backhaul networking products. It increases return-on-investment through reuse of the already available network hardware and support of the evolution of radio technologies. The proposed design allows rapid technology transition and commercialization success, thereby accelerating the fielding of capabilities to soldiers and to benefit the nation through improved wireless network product performance. The proposed NCNT can also increase the profit for Army and Navy by offering network coding software packages in the mobile ad hoc battlefield networks. For example, we expect the network capacity of the airborne fighter platforms can be increased by 10% by using our developed algorithms in the practical airborne networks. This result is significant and very exciting for supporting mission operations more effectively. The proposed multicast and unicast coding software packages (i.e., LPMC and OUC) can be implemented in the Airborne Fighter Platforms and Air/Space-based C4ISR platforms. We are contacting Boeing for possible support and collaboration and Boeing starts the internal review of the proposed work. NCNT increases the profit of other commercial airborne networks and wireless network providers through cost reduction and development of new revenue by offering cost-effective wireless communication. NCNT offers significant performance gains in saving wireless bandwidth in increasingly complex and dynamic airborne and ad hoc network environments. We are contacting AT&T for possible support and collaboration.

Principal Investigator:

Bin Xie

Business Contact:

Bin Xie
Small Business Information at Submission:

InfoBeyond Technology LLC
1211 Mallard Creek Road Louisville, KY 40207

EIN/Tax ID: 262783072
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
University of Louisville
501 East Broadway, Suite 200
Louisville, KY 40202
Contact: Michael P. Shannonhouse
Contact Phone: 5028528359