With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based on...With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.展开更多
Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get ac...Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission.In contrast to state-of-the-art designs focusing on the instantaneous cost of the network,this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot.Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process(MDP).Unfortunately,solving such an MDP problem with the conventional relative value iteration(RVI)can suffer from the curses of dimensionality,in the presence of a large number of users.As a countermeasure,we propose a distributed RVI algorithm to reduce the dimension of the MDP problem,such that the original problem can be decoupled into multiple solvable small-scale MDP problems.Simulation results reveal that the proposed algorithm can yield lower longterm average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.展开更多
文摘With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61901216,61631020 and 61827801the Natural Science Foundation of Jiangsu Province under Grant BK20190400+1 种基金the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2020D08)the Foundation of Graduate Innovation Center in NUAA under Grant No.KFJJ20190408.
文摘Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission.In contrast to state-of-the-art designs focusing on the instantaneous cost of the network,this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot.Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process(MDP).Unfortunately,solving such an MDP problem with the conventional relative value iteration(RVI)can suffer from the curses of dimensionality,in the presence of a large number of users.As a countermeasure,we propose a distributed RVI algorithm to reduce the dimension of the MDP problem,such that the original problem can be decoupled into multiple solvable small-scale MDP problems.Simulation results reveal that the proposed algorithm can yield lower longterm average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.
