NDLI: Towards network invariant fault diagnosis in MANETs via statistical modeling: The global strength of local weak decisions

Content Provider	IEEE Xplore Digital Library
Author	Vashist, A. Izmailov, R. Manousakis, K. Chadha, R. Chiang, C.J. Serban, C. Ali, S.E.
Copyright Year	2012
Description	Author affiliation: Knowledge-Based Systems Department, Applied Communication Sciences, Piscataway, NJ 08854, USA (Vashist, A.; Izmailov, R.; Manousakis, K.; Chadha, R.; Chiang, C.J.; Serban, C.) \|\| U.S. Army Communications Electronics Research, Development, and Engineering Center (CERDEC), Aberdeen Proving Ground, MD, 21005, USA (Ali, S.E.)
Abstract	Due to its obvious importance, fault detection and localization is a well-studied problem in communication networks, as attested by the many techniques designed to address this problem. The inherent variability, limited component reliability, and constrained resources of MANETs (Mobile Ad hoc Networks) make the problem not just more important, but also critical. Practical development and deployment considerations imply that fault detection and localization methods must i) avoid relying on overly detailed models of network protocols and traffic assumptions and instead rely on actual cross-layer measurements/observations, and ii) be applicable across different network scales and topologies with minimum adjustments. This paper demonstrates the feasibility of such goals, and proposes an important and as yet unexplored approach to fault management in MANETs: network-invariant fault detection, localization and diagnosis with limited knowledge of the underlying network and traffic models. We show how fault management methods can be derived by observing statistical network/traffic measurements in one network, and subsequently applied to other networks with satisfactory performance. We demonstrate that a carefully designed but widely applicable set of local and weak global indicators of faults can be efficiently aggregated to produce highly sensitive and specific methods that perform well when applied to MANETs with varying sizes, topologies, and traffic matrices.
Starting Page	981
Ending Page	987
File Size	357362
Page Count	7
File Format	PDF
ISBN	9781467302678
ISSN	15421201
e-ISBN	9781467302692
e-ISBN	9781467302685
DOI	10.1109/NOMS.2012.6212018
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Ad hoc networks Mobile computing Network topology Fault diagnosis Topology Accuracy supervised learning fault detection fault localization MANETs local fault indicators global fault detection
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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