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Predictable, Scalable QoS Routing for Ad Hoc Wireless Networks based on…

Award Information

Department of Defense
Award ID:
Program Year/Program:
2005 / STTR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive Suite 400 Rockville, MD 20855-
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Woman-Owned: Yes
Minority-Owned: No
HUBZone-Owned: No
Phase 1
Fiscal Year: 2005
Title: Predictable, Scalable QoS Routing for Ad Hoc Wireless Networks based on Heavy-Tailed Statistics
Agency / Branch: DOD / ARMY
Contract: W911NF-05-C-0079
Award Amount: $100,000.00


In this proposal, we identify QoS metrics based on our insights on heavy-tailed phenomena in ad hoc wireless networks, determine the potential performance impact of heavy-tailedness on ad hoc routing, define routing policies based on such insights and metrics, and propose a QoS Routing protocol (HAQR) that captures the heavy-tailed nature and provides quality of service for multi-hop wireless ad hoc networks. Performance metrics are identified as the 1st and 2nd order statistics of throughput, delay and packet loss. With respect to heavy-tailed traffic, L1 norm based estimation methods are exploited for the metrics estimation. The unique predictability nascent in heavy-tailed statistics is utilized to classify short- and long-lived flows and predict into the future for routing optimality and stability. In addition, time scales need to be carefully selected to ensure routing stability. HAQR possesses the advantages of both proactive and reactive routing paradigms and it has the following desirable properties: it (1) captures the heavy-tailed based metrics for routing decision, (2) adapts to the dynamic environment and accommodates the imprecise topology information, (3) reserves and releases bandwidth efficiently, and (4) reduces initial latency and routing overhead. The proposed algorithms for heavy-tailed based QoS routing are predictable, efficient and scalable.

Principal Investigator:

Hongjun Li
Senior Scientist

Business Contact:

Mark James
Contracts and Proposals Manager
Small Business Information at Submission:

Intelligent Automation, Inc.
15400 Calhoun Drive, Suite 400 Rockville, MD 20855

EIN/Tax ID: 521497192
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
Hovde Hall of Administration, 610 Purdue Mall
West Lafayette, IN 47907
Contact: Kihong Park
Contact Phone: (785) 494-7821
RI Type: Nonprofit college or university