28-09-2013, 12:12 PM
Fault Localization Using Passive End-to-End Measurement and Sequential Testing for Wireless Sensor Networks
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ABSTRACT:
Faulty components in a network need to be localized and repaired to sustain the health of the network. In this paper, we propose a novel approach that carefully combines active and passive measurements to localize faults in wireless sensor networks. More specifically, we formulate a problem (OFB) optimal sequential testing guided by end-to-end data.
This problem determines an optimal testing sequence of network components based on end-to-end data in sensor networks to minimize testing cost. We prove that this problem is NP-hard and propose a greedy algorithm to solve it. Extensive simulation shows that in most settings our algorithm only requires testing a very small set of network components to localize and repair all faults in the network. Our approach is superior to using active and passive measurements in isolation. It also outperforms the state-of-theart approaches that localize and repair all faults in a network.
OBJECTIVE:
we propose a novel approach that uses active measurement to resolve ambiguity in passive measurement, and uses passive measurement to guide active measurement to reduce testing cost we formulate a problem of optimal sequential testing guided by end-to-end data.
EXISTING SYSTEM:
A deployed sensor network may suffer from many network-related faults, failure or lossy nodes or links. These faults affect the normal operation of the network, and hence should be detected, localized and corrected/repaired. It has to test all identified components because some of them may be false positives. Hence the number of tests is at least equal to the total number of faulty components, while our approach requires much less number of tests.
On the other hand, it poses the challenge of fault inference since a faulty end-to- end behavior only indicates that some components are faulty and does not dictate exactly which components are faulty. Accurate inference from end-to-end data (i.e., locating all faults with low false positives) is not always possible because end-to-end measurement can have inherent ambiguity.
PROPOSED SYSTEM:
we propose a novel approach that uses active measurement to resolve ambiguity in passive measurement, and uses passive measurement to guide active measurement to reduce testing cost we formulate a problem of optimal sequential testing guided by end- to-end data. This problem determines an optimal testing sequence of network components that minimizes the total testing cost: it picks the first component to be tested (through active measurement), based on the test result (i.e., it is faulty or not faulty) and the end-to-end data.
Our approach differs from existing studies on sensor network fault localization in that it carefully combines active measurement and end-to-end data. The study in also uses end-to-end data together with active measurement: it uses end-to-end data to detect faults.
Two existing studies combine end-to-end measurement and active testing to identify and correct all faults in a network.
(i) our sequential tests are on individual components (instead of multiple components) with the guidance of end-to end data that provide insights into multiple components; and
(ii) we detect multiple faults instead of a single fault that is often assumed in other fields.