Self-Organized Network Management of UAV Based Internet of Flying Things

The integration of UAVs and IoT to perform various task has led to the emergence of Internet of Flying Things (IoFT). The mobile nature of unmanned aerial vehicles (UAVs) leads to frequent changes in topology of IoFT resulting in inefficient communication. To achieve efficient communication and better management of network topology, we propose self-organized network management (SONM) for IoFT using GSO. In the propose scheme, CH is selected based on UAV’s connectivity with Base Station (BS) as well as fitness which is determined by UAV’s energy level and its position. The cluster is managed as per the behavioral study of Glowworm Swarm Optimization by updating UAVs’ position based on luciferin value. Further we propose route selection mechanism which uses UAV’s residual energy, distance among UAVs and number of neighboring UAVs for effective communication. The performance of our propose SONM is compared with GWO and ACO based clustering scheme on the considered evaluation benchmarks.