Abstract: According to a technical aspect of the invention, there is provided a multiple unmanned aerial vehicles navigation optimization method is performed at a ground base station which operates in conjunction with unmanned aerial vehicles-base stations which are driven by a battery to move and cover a given trajectory point set, the multiple unmanned aerial vehicles navigation optimization method including: calculating an age-of-information metric by receiving an information update from the unmanned aerial vehicles-base stations through communication, when the ground base station is present within a transmission range of the unmanned aerial vehicles-base stations; setting conditions of a trajectory, energy efficiency, and age of information of each of the unmanned aerial vehicles-base stations; and executing Q-learning for finding a trajectory path policy of each of the unmanned aerial vehicles-base stations, so as to maximize total energy efficiency of an unmanned aerial vehicles-base station relay network to whic

Abstract: According to a technical aspect of the invention, there is provided a multiple unmanned aerial vehicles navigation optimization method is performed at a ground base station which operates in conjunction with unmanned aerial vehicles-base stations which are driven by a battery to move and cover a given trajectory point set, the multiple unmanned aerial vehicles navigation optimization method including: calculating an age-of-information metric by receiving an information update from the unmanned aerial vehicles-base stations through communication, when the ground base station is present within a transmission range of the unmanned aerial vehicles-base stations; setting conditions of a trajectory, energy efficiency, and age of information of each of the unmanned aerial vehicles-base stations; and executing Q-learning for finding a trajectory path policy of each of the unmanned aerial vehicles-base stations, so as to maximize total energy efficiency of an unmanned aerial vehicles-base station relay network to whic

Abstract: The present disclosure relates to a method for generating an energy demand model by a multi-access edge server, the method including: a step a of receiving a task execution request signal including a bit value from a user; a step b of matching a task to one or more pre-classified groups using the bit value; a step c of extracting a feature of energy consumed to perform the task according to a criterion set differently for each group; a step of performing the steps a to c for one or more task execution request signals received from one or more users during a time period and generating an energy demand pattern for the time period using a feature of energy for one or more tasks performed during the time period; and a step of generating an energy demand model by time using one or more energy demand patterns by time and energy cost by time.

Abstract: The present disclosure relates to a method for generating an energy demand model by a multi-access edge server, the method including: a step a of receiving a task execution request signal including a bit value from a user; a step b of matching a task to one or more pre-classified groups using the bit value; a step c of extracting a feature of energy consumed to perform the task according to a criterion set differently for each group; a step of performing the steps a to c for one or more task execution request signals received from one or more users during a time period and generating an energy demand pattern for the time period using a feature of energy for one or more tasks performed during the time period; and a step of generating an energy demand model by time using one or more energy demand patterns by time and energy cost by time.