Unmanned aerial vehicle localization for device-to-device communication in fifth generation networks using modified penguin search optimization

The localization of multiple Unmanned Aerial Vehicle (UAV) in Device to Device (D2D) communication has been investigated for Internet of Things (IoT) based Fifth Generation (5 G) network. The localization in heterogeneous UAV-D2D network is formulated using bioinspired Modified Penguin Search Optimization (MPGSO) algorithm. In MPGSO, the reduction in localization error is carried by tracking the position of UAV. The intra-UAV and inter-UAV communication search strategy is realized in accordance to inter-group and intra-group communication. The proposed localization by means of UAV exploration allows the fast convergence towards the local optimum. In addition to intensification in MPGSO, the diversification of nodes leads to the exploitation of UAV nodes that have travelled from the actual position. This avoids the convergence towards the local optima. The localization with MPGSO provide an improvement of 3.7%, 5.75% and 7.8% as compared to conventional artificial bee colony, particle swarm optimization and genetic algorithm, respectively.