Hybrid skill one-to-one-based optimization enabled trajectory planning in Internet of Things

Abstract

With the advancement of communication technology, unmanned aerial vehicles (UAVs) have been extensively utilized for attaining prolonged exposure to the Internet of Things (IoT). Due to the high sensitivity nature of sudden path changes, obstacle interference, and limited adaptability to dynamic environments, the performance of the UAV trajectory remains poor. To solve the above issues, this research proposed a hybrid optimization model called Skill One-to-One-Based Optimization (SOOBO) to initially generate an optimal, constraint-aware trajectory. This model employed a trajectory correction mechanism to adjust the path to avoid potential collision. Initially, the UAV-IoT model is taken into account, with trajectory generation incorporating both range constraints and collision avoidance among UAVs. The proposed SOOBO is employed for generating the feasible trajectory. Here, the SOOBO is obtained by the integration of a Skill Optimization Algorithm (SOA) and a One-to-One-Based Optimizer (OOBO). OOBO is a metaheuristic approach that effectively resolves optimization issues through iterative processes. The OOBO effectively solves the optimization issues and provides effectual quasi-optimal solutions. To attain a more effectual solution with faster convergence speed, the SOA is added to OOBO. SOA is based on the inspiration from an individual’s desire for learning and improving their knowledge. The SOA covers two stages termed as exploration and exploitation. Moreover, trajectory correction is performed to avoid collision between UAVs and obstacles. For attaining a better trajectory, the inscribed circle (IC) smooth method is utilized. Moreover, the performance measuring parameters like path length, speed, residual energy, and fitness are considered to estimate the performance of SOOBO-based trajectory planning in IoT, in which the finest outcomes of 12.54, 20.97m/s, 0.412 J, and 0.792 are attained.