公开(公告)号：US20230062221A1

公开(公告)日：2023-03-02

申请号：US17874313

申请日：2022-07-27

Applicant: UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA

Inventor： Gang SUN ,  Qing LI ,  Yuhui WANG ,  Hongfang YU ,  Jian SUN ,  Jing REN

IPC: H04L47/83 ,  H04L41/147

Abstract: A dynamic network resource allocation method based on network slicing is provided. A historical resource demand dataset of an accessed network slice is inputted into a first neural network for training. Based on a trained first neural network and the historical resource demand of the accessed network slice, a resource demand prediction information corresponding to the accessed network slice in a first prediction time period is determined. Resources are pre-allocated to the accessed network slice based on the resource demand prediction information, and resources are allocated to the accessed network slice when the first prediction time period arrives. In this way, the service provider can reasonably allocate network resources for network slices without violating the SLA, thus avoiding the waste of network resources.

公开(公告)号：US20230422140A1

公开(公告)日：2023-12-28

申请号：US18244925

申请日：2023-09-12

Applicant: University of Electronic Science and Technology of China

Inventor： Jing REN ,  Jianxin LIAO ,  Tongyu SONG ,  Chao SUN ,  Jiangong ZHENG ,  Xiaotong GUO ,  Sheng WANG ,  Shizhong XU ,  Xiong WANG

IPC: H04W40/10 ,  H04W40/20

CPC classification number: H04W40/10 ,  H04W40/20

Abstract: The present invention provides a method for optimizing the energy efficiency of wireless sensor network based on the assistance of unmanned aerial vehicle, firstly, collecting the state of the WSN through current routing scheme, and inputting the state of the WSN into the decision network of the agent to determine a next hover node; Secondly, based on the location of the next hover node, generating a new routing scheme by the UAV, and sending each sensor node's routing to its corresponding sensor node through current routing by the UAV; Lastly, after all sensor nodes have received their routings respectively, all sensor nodes send their collected data to the hover node through their routings respectively, and the UAV flies to and hovers above the next hover node to collect data through the next hover node, thus the data collection of the whole WSN is completed. Considering that the amounts of data forwarded by the sensor nodes are different, the rates of energy consumptions of the sensor nodes are also different, an online determination of the data collection scheme is adopted. When the residual energies of the sensor nodes relatively have changed, the UAV needs to determine a next hover node and generate a new routing scheme according to current state of the WSN, thus the energy efficiency of wireless sensor network is optimized and the lifetime of the WSN is maximized.

公开(公告)号：US20230231796A1

公开(公告)日：2023-07-20

申请号：US18125881

申请日：2023-03-24

Applicant: UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA

Inventor： Jing REN ,  Tongyu SONG ,  Jiangong ZHENG ,  Xiaotong GUO ,  Xuebin TAN ,  Sheng WANG ,  Shizhong XU ,  Xiong WANG

IPC: H04L45/02 ,  H04W40/10 ,  H04W40/24

CPC classification number: H04L45/08 ,  H04W40/10 ,  H04W40/24

Abstract: A method for energy efficient routing in wireless sensor network based on multi-agent deep reinforcement learning, predefines a to-be-deployed wireless sensor network and creates a cooperative routing decision system including A decision networks and one sink module, A decision networks deployed on the agents ai, i=1, 2, . . . , A, of the sensor nodes, the sink module deployed on the sink node n0. The decision network obtains a probability vector according to its local observation and position vectors. The sink module calculates a routing for each sensor node according the probability vectors of A decision networks and sends the routings to corresponding sensor nodes. A multi-agent deep reinforcement learning algorithm is adopted to train the decision networks of A agents ai, i=1, 2, . . . , A of the cooperative routing decision system, deploys the to-be-deployed wireless sensor network into an environment and updates the routing policy of the deployed wireless sensor network at each update cycle of routing.