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Proceedings Paper

Locating algorithm being needless to harbor based on the quality in wireless sensor networks
Author(s): Mirali Sharifi; Fatemeh Tousi; Tofiq Kazimov
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Paper Abstract

From locating techniques in the wireless sensors networks two groups based on harbor and without it can be referred to. Firstly, the harbor nodes distribute the local information in the network and through that the average distance between two groups or the average length of a step is identified. Non-harbor nodes know the shortest path as the number of steps to each of the harbors and determine their distance to the harbors by understanding this average step length and using this estimation compute their location distance. Firstly, the network nodes are clustered. Each harbor is a cluster head and the cluster members using information derived from this cluster head begin locating. This process starts by the nodes located in the common field between two clusters. Although algorithm comparability based on harbor is increased by the nodes clustering, but the algorithm precise and efficiency is still dependent on the number of harbor nodes. Using harbor in all of the conditions causes its usage limitation in the wireless sensor networks. Regarding the algorithms without needing to harbor, algorithm is the first case. This algorithm has invented a new method to make a local graph in the network which is applicable in computing the relative features of nodes. Firstly, each node makes a graph with its own axis. Then the general graph of network is made and each node changes its coordinates by using an algorithm. Because of the current limitations in the trigonometry method used in this algorithm, the computed coordinates are not reliable and face difficulties in many cases. The other algorithms being needless to harbor try to use another methods instead of trigonometry methods in locating. For instance, among these methods, those ones based on graph drawing or mass and coil algorithms can be referred to. These kinds of algorithms take much time and use a lot of energy. In order to upgrade the algorithm results quality and prevent the fault distribution, we define a secondary parameter called the computed location accuracy. This parameter indicates location accuracy and can be a value between zero and one.

Paper Details

Date Published: 13 January 2012
PDF: 5 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83500D (13 January 2012); doi: 10.1117/12.920254
Show Author Affiliations
Mirali Sharifi, Islamic Azad Univ. (Iran, Islamic Republic of)
Azerbaijan National Academy of Sciences (Azerbaijan)
Fatemeh Tousi, Islamic Azad Univ. (Iran, Islamic Republic of)
Azerbaijan National Academy of Sciences (Azerbaijan)
Tofiq Kazimov, Islamic Azad Univ. (Iran, Islamic Republic of)
Azerbaijan National Academy of Sciences (Azerbaijan)


Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies

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