Beijing University of Posts and Telecommunications

Qiuyan Yao

Jie Zhang

Hui Yang

Bowen Bao

Chao Li

Zhengjie Sun

Lin Guan

Shanguo Huang

Shuai Dong

Daqing Meng

Ao Yu

Disclosed are a low congestion routing method of an Optical Network-on-Chip (ONoC) and related devices. In the method, a source IP node sends a route setup request to a first electrical router; the first electrical router determines a first target output port and sends the request to the destination IP node through the first target output port; the destination IP node generates an ACK packet and sends the ACK packet to a second electrical router; the second electrical router determines a second target output port and sends the ACK packet to the source IP node through the second target output port; the source IP node transmits the ACK packet to an optical transmission port, performs an E/O conversion on the ACK packet and sends an optical information packet through optical routers to the destination IP node; the destination IP node performs an O/E conversion on the optical information packet.

Low congestion routing method of Optical Network-on-Chip and related device

A routing method includes: determining a path quality between a first node and each of second nodes in a service to be transmitted through a path quality evaluation model; where, the second node is one next-hop node of the first node; and the path quality evaluation model is constructed according to a signal-to-noise ratio SNR and an ambient temperature change; determining an optimal next-hop node from second nodes according to the path quality; updating a Q table of the first node according to the optimal next-hop node; taking the optimal next-hop node as a new first node; returning to the step of determining a path quality between a first node and each of second nodes until the new first node is a destination node of the service to be transmitted; and determining a transmission path of the service to be transmitted according to the Q table.

Routing method and related device

Disclosed is a method of network resource allocation. The method includes: generating an adjacency matrix of nodes in a metropolitan area network (MAN) according to spatial adjacency relationships of the nodes; generating a network state feature matrix according to traffic information of each node; extracting traffic spatial features of the nodes from the adjacency matrix and the network state feature matrix through a traffic spatial feature extraction model; obtaining predicted traffic of the nodes from the traffic spatial features through a traffic prediction model; and performing a network resource allocation according to the predicted traffic of the nodes. Further, a device of network resource allocation and a non-transitory computer-readable storage medium are also disclosed.

Method and device of network resource allocation

Disclosed is a cross-chain communication method. In this method, a first edge node of a first blockchain receives a first transaction request and validates the first transaction request; on condition that the first transaction request passes the validation, the first edge node will send the first transaction request to a notary node group; the notary node group determines at least one target notary node according to a preset criterion; the at least one target notary node validates the first edge node and a second edge node of a second blockchain corresponding to the first transaction request; and on condition that both the first edge node and the second edge node pass the validation, the first edge node will set up a cross-chain communication with the second edge node depending on the first transaction request to complete a cross-chain transaction. A cross-chain communication device and a storage medium are also disclosed.

Cross-chain communication method, device and storage medium thereof

Disclosed is a method for network slicing, which includes: determining a service keyword of a service intention after receiving the service intention from a user; converting the service intention into a network request according to the service keyword of the service intention on the basis of a pre-established mapping model, wherein, the network request comprises at least one network requirement index; determining a set of network slicing configuration parameters according to the network request and current network environment state on the basis of a network slicing model, wherein the network slicing model is a deep neural network model for determining network slicing configuration parameters according to a network request and a network environment state; and performing a network slicing according to the set of network slicing configuration parameters. The present disclosure also provides a device for network slicing and a computer readable storage medium.

Method for network slicing, device and storage medium thereof

Disclosed is a data sharing method, including: building a trust alliance block chain, the trust alliance block chain comprising a main chain and at least one slave chain; each slave chain corresponding to a domain; and each domain comprising a leader node; establishing a virtual slave chain on the trust alliance block chain; designating, by the leader node in each domain, a node as a federated learning node; assigning, by the leader node in each domain, a virtual identity to the federated learning node to join the federated learning node to the virtual slave chain; and performing, by each federated learning node, a joint training on a local federated learning model using data generated in its own domain to establish a public federated learning model, through which data are shared among the domains. The present disclosure further provides a data sharing system, an electronic device and a storage medium.

Data sharing method, system, electronic device and storage medium thereof

A system of traffic prediction for IoT nodes includes at least one access node, a transmission network and a cloud platform. The access node is configured to collect traffic data, cluster the traffic data into access traffic data and network traffic data, input the access traffic data into an access traffic prediction model to obtain a prediction result of the access traffic at a next moment, and upload the network traffic data and the prediction result of the access traffic to the cloud platform; the cloud platform is configured to input the network traffic data into a network traffic prediction model to obtain a prediction result of the network traffic for each access node at the next moment, and obtain a prediction result of traffic for each node according to the prediction result of the network traffic and the prediction result of the access traffic.

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Beijing, China

Qiuyan Yao