Fig. 18: Percentage of localization error vs. Total Number of
Nodes with 0-30% ranging error.
As node density per unit area increases with an increase in the total
number of nodes, it makes the unknown nodes closer to more anchor nodes.
This way the lesser hop counts determines the distance \(d_{i}\) between
the anchor node and the unknown node with less error is governed by
equation (2) and (23). The more appropriate distance estimation\(d_{i}\) contributes a better location estimation for the DV-Hop
algorithm and IDV. Similarly, ODR improves due to more anchor nodes fall
in the minimum distant anchor nodes set \({}^{\prime}K^{\prime}\) and hence the centroidCNÂ can indicate a probable location for an intended unknown
node with more precision.
The Experiment 3 reveals that the proposed model ODR is better than
DV-Hop algorithm and IDV by approximately 7% and 4% respectively in
the ideal case as shown by Fig. 15 while the improvement is
approximately 10%, 14%, 20% and 8%, 10%, 12% in comparison to
DV-Hop algorithm and IDV respectively by considering the ranging error
slabs of 0-10%, 0- 20%, and 0-30% respectively as presented through
Fig. 16, 17, and 18.