Dual Decision Multi-UAV Parallel Auxiliary IoT Data Acquisition Time Optimization

Drones have been widely used in Internet of Things tasks, and rational planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impacts. Existing of existing UAV data acquisition efforts take all efficiency and safety goals into a single decision, And view it as a multi-objective optimization problem, While the single-decision model ignores the connection between the optimization goals and multiple decision-makers, For this purpose, for the first time, the efficiency-based DM (decision-maker, DM) and safe DM UAV multi-decision multi-target trajectory optimization (BP-UAVTO, Biparty Multiobjective UAV Trajectory Optimization) Model, For this problem, Improvement to the existing multi-target immunization algorithms, BP-series algorithm to find the Pareto optimal solution of the BP-UAVTO model, As close as possible to the Pareto front of each decision. Then it is decoupled into a problem of data acquisition time minimization in the wide area Internet of Things without accurate SN location information, and a greedy algorithm based on the division unit (Greedy algorithm based on dividing units, GA-BODN) is proposed to minimize the completion time of parallel data acquisition of multiple drones in a large range. Simulation results show that the Pareto optimal solution of BP-algorithm is closer to the Pareto front of each decision than the ordinary multi-objective evolution algorithm such as NSGA-II and the multi-decision multi-objective evolution algorithm such as OptMPNDS and OptMPNDS2. The GA-BODN algorithm outperformed the average simulation time by the statistical simulation method and was close to the lower bound on the completion time of data acquisition.