DAAPEO: Detect and Avoid Path Planning for UAV-Assisted 5G Enabled Energy-Optimized IoT

Unmanned Aerial Vehicles (UAVs) have been making an indelible mark on the automation industry by meeting the stringent standards of Fifth Generation (5G) connectivity for seamless data dissemination from Internet of Things (IoT). However, the limited battery resources of IoT Sensor Devices (ISD), collision free flight operation of swarm of UAVs i.e., multiple UAVs flying at the same time, are challenging concerns which need to be given attention. In this work, the proposed work addresses the aforementioned issues by proposing an energy-optimized data dissemination strategy for the IoT and a pre-determined path planning strategy for collision-free UAV flight operation, the proposed work is reffered as DAAPEO. The boosted sooty tern optimization is used for selecting the Cluster-Head (CH) in IoT being deployed with a large number of ISDs. Following the selection of the CH, two UAVs are programmed to hover in a pre-determined path, collecting data from the corresponding CHs in their immediate vicinity. The proposed idea is decentralized when it comes to choosing a CH and centralized when it comes to UAVs path planning. For collision avoidance with the UAV or other obstacles, a Light Detection and Ranging (LiDAR) sensor is used for the former, and deterministic path planning is done for the latter. Simulation results showcase the predominance of proposed work (i.e., DAAPEO) over the competitive methods, as it essentially improves the energy efficiency of 5G IoT and also helps in Detect and Avoid (DAA) path planning for avoiding the collision of launched UAV s within themselves or with other objects.