The authenticity of information is the key security factor of system in time-sensitive networking (TSN). However, the direct introduction of traditional security authentication mechanism will lead to a significant reduction in schedulability of the system. The existing methods still have the problems of few application scenarios and high resource consumption. To address this problem, a security-aware scheduling method for TSN was proposed. Firstly, based on the traffic characteristics of TSN, a time-efficient one-time signature security mechanism was designed to provide efficient multicast source authentication for messages. Secondly, the corresponding security model was proposed to evaluate the mechanism and describe the impact of the security mechanism on tasks and traffic. Finally, the proposed security-aware scheduling method was modeled mathematically. On the basis of traditional scheduling constraints, some constraints related to security mechanisms were added. At the same time, the optimization objective was to minimize the end-to-end delay of applications, and constraint programming was used to solve the problem. Simulation results show that the introduction of the improved one-time signature mechanism can effectively protect the authenticity of key information in TSN, and has limited impact on scheduling. In multiple test cases of different sizes generated based on real industrial scenarios, the average end-to-end delay and bandwidth consumption of the generated applications only increased by 13.3% and 5.8% respectively. Compared with other similar methods, this method consumes less bandwidth, thus more suitable for TSN networks with strict bandwidth restrictions.

In order to adapt to the limited computing performance and energy of numerous lightweight sensor nodes in the encrypted transmission of IoT (Internet of Things), this paper proposed a fast modulus algorithm (CZ-Mod algorithm) based on Mersenne-like numbers to slove the bottleneck problems of computing speed, power consumption and so on during the sensors run PKI (Public Key Infrastructure) encryption algorithms such as RSA (Rivest-Shamir-Adleman), DHM (Diffie-Hellman-Merkle), Elgamal, etc. , and to simplify the corresponding hardware encrypting circuit logic design. CZ-Mod algorithm uses the mathematic characteristics of Mersenne numbers, and lowers the time complexity of its essential operation mod (modulo) into O(n). Firstly, a fast modulus algorithm mod1 using Mersenne-like numbers as modulus was presented, changing complex mod operation into simple binary shift/add operation; secondly, a fast modulus algorithm mod2 using any positive integers near Mersenne-like numbers as modulus was presented, expanding the modulus value range while simplifying mod operation; and then logic circuits of mod1 and mod2 operations were designed, simplifying mod operation hardware circuit. Finally, the above work was applied to the key exchange of IoT nodes, so as to lower the computing complexity and improve the speed of PKI encryption algorithms. The experiment test results indicate that the speed of DHM key exchange with CZ-Mod algorithm can reach 2.5 to 4 times of that of the conventional algorithm; CZ-Mod algorithm is concise and fits the hardware circuit design for the IoT sensors.

As a new generation technology of Ethernet, time sensitive networking (TSN) plays an increasingly important role in industrial control, vehicle network and other fields, as it guarantees low-delay and low-jitter transmission of time sensitive traffic. As one of the key shaping technologies of TSN, credit-based shaping (CBS) guarantees the deterministic transmission of audio video bridging (AVB) traffic by reserving bandwidth. The existing bandwidth allocation methods based on network calculus mostly do not consider the impact of routing on the schedulability of AVB traffic, and the bandwidth allocation results are poor and the solution time is long when the network is in a large scale. Therefore, this paper proposed a bandwidth allocation method based on link load balancing. Firstly, the link load balancing routing algorithm was used to calculate the optimal path for each AVB traffic. Then, based on the flow path and network calculus, the arrival and service curves of each switch’s outbound port traffic were analyzed to obtain the worst-case forwarding delay. Finally, the optimization objectives and constraints for bandwidth allocation were established, a heuristic algorithm was used to solve bandwidth allocation, and the bandwidth parameter configuration was optimized during the solving process. The experimental results show that the AVB traffic bandwidth allocation method based on link load balancing can improve the schedulability of AVB traffic by 15 to 45 percentage points, compared with existing bandwidth allocation methods. Optimizing parameter configuration can increase the bandwidth solving speed by more than twice and obtain better bandwidth allocation results, which can effectively cope with large-scale dynamic changes in TSN networks.

Knowledge graph provides underlying support for many intelligent information service applications, including intelligent search, public safety, finance, medical care and other fields. However, the existing knowledge graph is usually incomplete, and knowledge graph completion has become an urgent problem to be solved. The existing knowledge graph completion method models ignore the important information rich in neighbor nodes and relationships, and often simply splice neighbor nodes and relationships together, ignoring the different importance of different relationships and neighbor nodes to nodes. To solve this problem, this paper proposed a knowledge graph completion method (ICGAT) based on the interactive connection graph attention network. The method firstly finds out the potential relationship by finding two-hop neighbor nodes, and expands the triples of each node. Then it fuses the relationship in each triplet with the features of the node, and adopts the method of interactive connection between nodes and neighbor nodes, using 4 space vectors to represent the interactively connected relationship. Finally, the vector of interactive connection was input into the graph attention network to obtain the weight of relations and neighbor nodes to the node, so as to illustrate its importance. In order to effectively represent triples of complex relationships such as one-to-many, many-to-many, etc. , this method used the RotatE model as a pre-training model. The experimental results in the link prediction task show that the performance of the mean rank and HR@10 indicators of the ICGAT method in the WN18RR and FB15k-237 datasets have been improved to a certain extent, indicating that ICGAT can improve the accuracy of the link prediction task.

With the progress of network technology, applications such as vehicle networks, industrial Internet of Things and 5G ultra-reliable low-delay communication (uRLLC) all require TSN to ensure ultra-low delay deterministic data transmission. TSN traffic scheduling requires a fast and accurate scheduling algorithm. The existing accurate solution methods are of high complexity and cannot meet the real-time requirements in large-scale joint scheduling. This paper designed a routing optimization genetic algorithm (Routing-GA) with better performance. Combining routing and traffic scheduling constraints, it can improve the efficiency of scheduling algorithm by optimizing routing and provide services for link load balancing scheduling. This strategy increases the space and flexibility of scheduling, and has the characteristics of fast near-optimal solution of meta-heuristic algorithm. It can deal with large-scale TSN routing constraint joint scheduling problem simply and effectively. Routing-GA takes the minimum end-to-end delay of time-sensitive flow as the optimization objective, considers Routing and TSN constraints jointly, and provides a genetic algorithm coding method with low complexity, high efficiency and high scalability according to the characteristics of TSN transmission problems. In addition, in order to improve the performance of the scheduling algorithm, a crossover mutation mechanism was proposed to optimize the route length and link load balancing. The experimental results show that the realized Routing-GA can effectively reduce the end-to-end delay and significantly improve the solution quality. The evolution rate can reach 24.42%, and the average iteration time of traditional genetic algorithm (GA) is only 12%. It can effectively improve the performance of the algorithm and meet the constraint requirements of TSN scheduling.