In order to achieve the optimal balance between task execution delay and energy consumption in Mobile Edge Computing (MEC) networks. First, the Analytic Hierarchy Process (AHP) is adopted to classify the priority of all tasks, so as to establish a related model for task offloading strategy and weight allocation of resources. Then, a multi-task offloading algorithm based on DNN is introduced to generate offloading strategies using multiple DNNS. Meanwhile, training samples composed of offloading strategies and input data are stored through the experience pool. These training samples will be used to train DNN. Simulation results show that the accuracy of the proposed multi-task offloading algorithm can reach 0.99, and the total delay of task processing and system cost can be effectively reduced compared with the three comparison algorithms.
Age of Information(AoI) is a novel metric to measure freshness of data in status update scenarios proposed by academia in recent years. Real-time applications need to transmit data packets for status update to the target node as soon as possible. However, due to the data density, the limited computing capacity of edge devices and the influence of the environment, the problems of intensive computation and high delay are caused. Mobile edge computing (MEC) is a new computing mode that extends cloud computing power closer to the user, where computing offloading and other technologies promise to solve those problems. We mainly studies the AoI optimization in MEC networks, in which data freshness and offloading strategy play an important role. Firstly, we propose the average AoI minimization problem for MEC network scenarios, and propose a multi-agent deep reinforcement learning(DRL) algorithm called Federated Multi-Agent Actor-Critic (Fed-MAAC). Federated learning is used to train agents to improve algorithm performance and data security. At the same time, we conducted experiments in gym, a popular simulation environment in reinforcement learning, and compared Fed-MAAC with baseline algorithm. The simulation results show that this algorithm is superior to other algorithms in average AoI optimization performance.
With the wide application of UAV (Unmanned Aerial Vehicle) in social life, the threat to social public security is increasing, and UAV countermeasures technology has gradually become a research hotspot. Firstly, according to the working process, UAV countermeasures can be divided into three types: detection and early warning technology, signal interference technology and interception and strike technology, and their principles are expounded. Finally, the advantages and disadvantages of countermeasures technology are analyzed. The problems existing in UAV countermeasures are analyzed from three perspectives of integration, collaboration and innovation, and the future construction of countermeasures is prospected.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.