2018
DOI: 10.9781/ijimai.2017.11.001
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Novel Clustering Method Based on K-Medoids and Mobility Metric

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Cited by 6 publications
(3 citation statements)
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“…In the four classifications, we have the first case that reflects a better stability for the node in question and the latter which represents a poor stability of the studied node, intuitively the second is better than the third, because N in is greater than N out and secondly, even with N div >N con the diverging node stay in the coverage area of the studied node. We determine subsequently metric degree of stability that will calculate for each category the best stability node, we use in the formula (7) the coefficient γof flow defined in [23], we divide the coefficient of 4 intervals as shown in Table 1, and metric of stability degree of node i will be as follows:…”
mentioning
confidence: 99%
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“…In the four classifications, we have the first case that reflects a better stability for the node in question and the latter which represents a poor stability of the studied node, intuitively the second is better than the third, because N in is greater than N out and secondly, even with N div >N con the diverging node stay in the coverage area of the studied node. We determine subsequently metric degree of stability that will calculate for each category the best stability node, we use in the formula (7) the coefficient γof flow defined in [23], we divide the coefficient of 4 intervals as shown in Table 1, and metric of stability degree of node i will be as follows:…”
mentioning
confidence: 99%
“…The stability metric is defined in our proposition formula (7) [23]. The disadvantage of this approach is that the parameter must be fixed between three values ( 0.25, 0.…”
mentioning
confidence: 99%
“…propose a new algorithm of clustering based on new mobility metric and K-Medoid to distribute nodes into several clusters, to avoid the problem of negative influence of MANETS on the performance of QoS [3].…”
mentioning
confidence: 99%