Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security

. 2022 Jun 16;22(12):4539. doi: 10.3390/s22124539

Algorithm 1 Fuzzy C-means clustering.

1:
parameters: $p, n, N, f m, p_{l}, p_{m}, D^{f m}, d$
2:
initialize: $p, n, N, f m, p_{l}, p_{m}, D^{f m}, d$
3:
randomly initialize the membership matrix:
$D i s t a n c e = [\begin{matrix} 0 & D i s t a n c e (1, 2) & \dots & D i s t a n c e (1, n) \\ ⋮ & 0 & \dots & ⋮ \\ D i s t a n c e (n, 1) & D i s t a n c e (n, 2) & \dots & 0 \end{matrix}]$
4:
calculate the centroid:
$Centroid ((p_{l} - q_{l}), (p_{m} - q_{m})) = (\frac{p_{l} + q_{m}}{2}, \frac{q_{l} + q_{m}}{2})$
5:
calculate the Euclidean distance:
$E U (p q) = \sqrt{{(p_{m} - p_{l})}^{2} + {(q_{m} - q_{l})}^{2}}$
6:
update the new membership matrix:
$o b j (f) = \sum_{k = 1}^{N} \sum_{j = 1}^{n} D_{(k j)}^{f m} \cdot d_{(k j)}^{2}$
7:
repeat
8:
step 4
9:
until centroids no longer change
10:
while the topology has changed do
11:
steps 1 to 6
12:
end while