Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks

. 2024 Dec 26;25(1):72. doi: 10.3390/s25010072

Algorithm 1: HAMO-based clustering.

Input : Number of UAVs N

, 3 D positions of UAVs P_{i} = (x_{i}, y_{i}, z_{i})

, velocity of UAVs

({V x}_{i}, {V y}_{i}, {V z}_{i}

), UAV residual energy E (i)

, and maximum energy level of UAVs E_{m a x}

Output : Initial clusters C_{k}

with CHs

# Step 1: Initialize population

1 : for each UAV i

in {1, 2, \dots, N}

do:

2 : initialize UAV [i

] with:

3 : I D : {I D}_{i}

, P_{i} = (x_{i}, y_{i}, z_{i})

({V x}_{i}, {V y}_{i}, {V z}_{i}

), and E (i)

4: end for

# Step 2: Initial cluster assignment

5 : for each UAV i

in {1, 2, \dots, N}

do:

6 : Calculate distance d_{i j}

, between UAV [i

] and all other UAVs j

using (1)

7 : Assign UAV [i

] to the nearest cluster C_{k}

based on d_{i j}

8: end for

# Step 3 : CH selection using local information 9 : for each cluster C_{k} do : 10 : for each UAV i in C_{k} do : 11 : Calculate fitness score for UAV i based on (11) 12 : Select the UAV with maximum fitness score F_{C H} (i) as CH 13 : end for # Step 4 : Output summary of cluster formation 14 : Output the list of clusters C_{k} with their corresponding CHs