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To detect this type of outlier nodes, we can first detect outlier edges.
After distinguishing the malicious nodes, we can obtain the unreasonable recommendation trust vector.
To model the sensor nodes, we can either use system-level simulation or instruction-level emulation.
With the spatial distribution of atoms in nodes, we can derive the distribution of distances between any pair of nodes.
We do not consider anchors, as we are interested here is how many nodes we can localize.
By using cooperative nodes, we can reduce the energy consumption of cluster heads efficiently and prolong the lifetime of network.
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With the existence of gateway node, we can easily establish the cluster structure for purpose.
If we have the cluster members for each node, we can integrate them into the transmission likelihood of NetRate model.
Once the calculation has been applied to every node, we can construct contours of the density index.
To check the congestion of a node, we can use the queue space available at each node and the mobility of node can be measured with the help of velocity.
Then, by analyzing the correlation coefficient between average recommendation trust vector and recommendation trust vector of each recommendation node, we can detect the malicious recommendation node.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com