Fig 1.
Clustering model in FANETs.
Fig 2.
Diagram of the proposed cooperative localization.
Fig 3.
α v.s. the variance of measurement noise.
Fig 4.
The flowchart of the FSICL algorithm.
Fig 5.
(a) Approaching model: UAV m is approaching UAV n from location b to location c. (b) Leaving model: UAV m is leaving UAV n from location b to location c.
Fig 6.
The flowchart of the FSIAC algorithm.
Fig 7.
Initial distribution and communication radii of UAVs.
(a) Initial locations and moving directions of UAVs (the arrow line represents the moving direction). (b) Initial communication radii of UAVs (the yellow sphere with the UAV as the center represents the communication range of the UAV).
Table 1.
Simulation parameters.
Fig 8.
Localization results with 6 anchors when the variance of noise is 1 Watt.
(a) 1000 estimated locations of a UAV by the Chan algorithm. (b) 1000 estimated locations of a UAV by FSICL. (c) Average estimated locations of 100 UAVs of 1000 iterations by the Chan algorithm. (d) Average estimated locations of 100 UAVs of 1000 iterations by FSICL.
Fig 9.
Fitness value v.s. iteration.
Fig 10.
Average RMSE results after 1000 iterations.
(a) RMSE v.s. the variance of noise. (b) RMSE v.s. the number of anchor.
Fig 11.
Connectivity of UAVs after different iterations.
(a) First itration. (b) Tenth itration.
Fig 12.
The variance of the number of UAVs in clusters v.s. iteration.
Fig 13.
The handover rates of the clusters v.s. UAV moving speed.
Fig 14.
Link expiration time of the clusters v.s. UAV number.
Fig 15.
Minimum node lifetime v.s. UAV number.
Table 2.
Performances of different localization algorithms.
Table 3.
Performances of different clustering algorithms.