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
Award Id:
74168
Agency Tracking Number:
A054-016-0249
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
161911532
Principal Investigator:
Hongjun Li
Senior Scientist
(301) 294-5275
jli@i-a-i.com
Business Contact:
Mark James
Contracts and Proposals Manager
(301) 294-5221
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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