Predictable, Scalable QoS Routing for Ad Hoc Wireless Networks based on Heavy-Tailed Statistics

Award Information
Agency:
Department of Defense
Branch:
Army
Amount:
$100,000.00
Award Year:
2005
Program:
STTR
Phase:
Phase I
Contract:
W911NF-05-C-0079
Agency Tracking Number:
A054-016-0249
Solicitation Year:
2005
Solicitation Topic Code:
A05-T016
Solicitation Number:
N/A
Small Business Information
INTELLIGENT AUTOMATION, INC.
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
Y
Duns:
161911532
Principal Investigator
 Hongjun Li
 Senior Scientist
 (301) 294-5275
 jli@i-a-i.com
Business Contact
 Mark James
Title: Contracts and Proposals Manager
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
 PURDUE UNIV.
 Kihong Park
 Hovde Hall of Administration, 610 Purdue Mall
West Lafayette, IN, 47907
 (785) 494-7821
 Nonprofit college or university
Abstract
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.

* information listed above is at the time of submission.

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