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Fault-Localization Protocol for Wireless Sensor Networks

delete2019-10-01
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PRE
AI
A
Ather Saeed *
M
M. Arif Khan
A
Ali Syed
DOI:10.1109/lcnsymposium47956.2019.9000658delete
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摘要

摘要

En 中文
Maintaining network connectivity for reliable acquisition of data is crucial when disseminating the sensed phenomena in wireless sensor networks. The use of Global Positioning System (GPS) is considered as the most reliable method when detecting the location of a particular node in the network. However, localisation techniques using GPS are extremely expensive when the sensor field is massively dense. Many researchers have designed alternative methods such as triangulation where beacon nodes are used for creating a reference point from the nearest neighbor for locating the nodes of interest. Such technique mostly relies on various estimates where chances of error are extremely high if there are problems with the beacon node that is used for creating a reference point. In this paper, we present a novel Green's Theorem (GT) based Fault-Localisation Protocol (GT-FLP) to identify the faulty nodes and their location in the network. The proposed technique, GT-FLP, uses partitioning and clustering methods for the dense sensor networks. Each cluster has a cluster-head for disseminating data-sets to the sink node. It is also assumed that the network is partitioned such that the neighboring edges of the next cluster are closer than their sensing radius. The sensor nodes also store their Received Signal Strength Indicator (RSSI) values and disseminate information about their battery levels and energy consumption rates. We performed the experiment to investigate, (i) how to efficiently identify faulty nodes and pinpoint their exact location, (ii) dynamically partitioning the network for reliability and adaptability, and (iii) provide mathematical formulation of the localisation problem in the context of wireless sensor networks.
Keyword:
Green's Theorem
Fault-Localisation
RSSI
WSN
Clustering
Fault-Tolerance
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期刊

I
IEEE Local Computer Networks Symposium on Emerging Topics in Networking
IF:
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论文数:
2
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机构

C
Charles Sturt University
学者数:
3.6K
论文数: 3.4K
被引数: 4.0K
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