Wei Liang

In-band Network Telemetry (INT) enhances real-time, high-resolution network monitoring capabilities by incorporating fine-grained internal state information into packets. Utilizing INT for network-wide visualization can significantly bolster network management and operation. Although existing studies have made significant contributions, they have not been able to simultaneously meet the following two objectives: 1) Comprehensive visibility of the network's internal state, which refers to obtaining information on all switch ports and their corresponding link states; 2) Low measurement overhead, which involves reducing measurement bandwidth overhead and minimizing the impact of the measurement process on the network. We designed INT-MC to meet both of these objectives. INT-MC is an efficient and cost-effective network-wide telemetry solution based on matrix completion. By modeling link metadata as a matrix and leveraging its low-rank property, INT-MC selectively measures certain links. It then employs matrix completion algorithms to infer information about unmeasured links, thereby achieving low-overhead network-wide telemetry. Designing paths to cover the target links involves an NP-complete problem, and the high computational complexity may delay the measurement tasks. To circumvent this, we propose an improved scheme based on Eulerian digraph decomposition, transforming the path calculation problem into a high-information path selection problem, significantly reducing computational costs. We have implemented an INT-MC prototype within the NSFNet topology, consisting of 14 Tofino switches and 10 end-hosts, and conducted extensive experiments and evaluations. The results indicate that INT-MC incurs only 16% of the measurement overhead compared to existing network-wide telemetry solutions, while achieving nearly identical accuracy. Even under high-frequency measurements of 20 times per second, the bandwidth overhead of INT-MC is approximately 0.075% of the total bandwidth, exerting minimal impact on the network.

To solve the perspective-n-point problem in visual measurement, we present a camera pose estimation algorithm involving weighted measurement uncertainty based on rotation parameters. The method does not involve the depth factor, and the objective function is converted into a least-squares cost function that contains three rotation parameters. Furthermore, the noise uncertainty model enables a more accurate estimated pose, which can be directly calculated without initial values. Experimental results prove the high accuracy and good robustness of the proposed method. In the space of 1.5m×1.5m×1.5m , the maximum estimation errors of rotation and translation are better than 0.04° and 0.2%.

Against the backdrop of the booming supply chain finance and logistics industry, Logistics 4.0 has emerged. However, the financing capacity of today's logistics companies is still unable to respond to the needs of the rapid development of the supply chain. On the one hand, the problem of logistics companies’ lack of existing high-value collateral in supply chain finance has led to a clutter of their credit data and difficulty in verifying their creditworthiness. On the other hand, the massive access to logistics companies’ business information can also lead to privacy leaks. At the same time, the transparency feature of blockchain is used to solve the financing dilemma of many industries in supply chain finance. On this basis, we propose a blockchain-based financing scheme (BFS) for logistics company. BFS utilises more efficient and interpretative smart contract technology, as well as improved privacy information query and invocation algorithms, to realise automatic control of entity node privacy information flow and significantly simplifying the steps and lowering the thresholds to financing for logistics companies, while protecting the privacy of their data. After extensive simulation testing, BFS can run supply chain blocks at a stable transaction throughput of around 280 RPS, with data transfers that meet the financing needs of logistics companies, providing a higher and more stable performance than the native Hyperledger Fabric.

The fast development of Blockchain technology makes it widely applied in several fields of digital transactions, like e-government affairs and the protection of financial transactions. In this paper, we propose a homomorphic encryption-based Blockchain for circuit copyright protection that effectively addresses the issues in the protection of circuit copyright transactions, such as low security of private data and low efficiency in transaction data storage, cooperation and supervision. First, we establish a homomorphic encryption-based mathematical model by utilizing Blockchain and intelligent contract, and next, the algorithms that include Blockchain generation, homomorphic chain encryption/decryption, and intelligent contract are designed. As the intelligent contract is correctly executed in Blockchain, a fully homomorphic encryption-based identity authentication protocol is tackled for Blockchain, given that it ensures the change operation of any third-party in Blockchain and realizes real-time verification. The system is apposite for circuit copyright protection in a blockchain network, due to the use of distributed identity authentication and real-time extensible storage improves the security and extensibility of blockchain-based circuit copyright protection. The experimental results show that the proposed algorithm has reduced the transmission cost and improved the efficiency of data storage and supervision.

With the fast advancements of electronic chip technologies in the Internet of Things (IoT), it is urgent to address the copyright protection issue of intellectual property (IP) circuit resources of the electronic devices in IoT environments. In this work, a fast deep reinforcement learning (DRL)-based detection algorithm for virtual IP watermarks is proposed, by combining the technologies of mapping function and DRL to preprocess the ownership information of the IP circuit resource. The deep Q-learning(DQN) algorithm is used to generate the watermarked positions adaptively, making the watermarked positions secure yet close to the original design, turning the watermarked positions secure. An artificial neural network (ANN) algorithm is utilized for training the position distance characteristic vectors of the IP circuit, in which the characteristic function of the virtual position for IP watermark is generated after training. In IP ownership verification, the DRL model can quickly locate the range of virtual watermark positions. With the characteristic values of the virtual positions in each lookup table (LUT) area and surrounding areas, the mapping position relationship can be calculated in a supervised manner in the neural network, as the algorithm realizes the fast location of the real ownership information in an IP circuit. Experimental results show that the proposed algorithm can effectively improve the speed of watermark detection as also reducing the resource overhead. Besides, it also achieves excellent performance in security.

The massive redundant data storage and communication in network 4.0 environments have issues of low integrity, high cost, and easy tampering. To address these issues, a secure data storage and recovery scheme in the blockchain-based network is proposed by improving the decentration, tampering-proof, real-time monitoring and management of storage systems, as such design supports the dynamic storage, fast repair, and update of distributed data in the data storage system of industrial nodes. A local regenerative code technology is used to repair and store data between failed nodes while ensuring the privacy of user data. That is, as the data stored are found to be damaged, multiple local repair groups constructed by vector code can simultaneously yet efficiently repair multiple distributed data storage nodes. Based on the unique chain storage structure, such as data consensus mechanism and smart contract, the storage structure of blockchain distributed coding not only quickly repair the nearby local regenerative codes in the blockchain but also reduce the resource overhead in the data storage process of industrial nodes. Experimental results show that the proposed scheme improves the repair rate of multi-node data by 9% and data storage rate increased by 8.6%, indicating to be promising with good security and real-time performance.