Showing papers by "Yueming Li published in 2022"

Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach

Abstract: A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor–critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.

A controllable privacy data transmission mechanism for Internet of things system based on blockchain

Abstract: With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.

Machine Learning-Empowered Beam Management for mmWave-NOMA in Multi-UAVs Networks

Abstract: We consider a mission-driven multiple unmanned aerial vehicles (multi-UAVs) network with millimeter-wave (mmWave) transmissions, where a leader UAV (LUAV) communicates with a large number of follower UAVs (FUAVs) simultaneously via a uniform planar array with only a limited number of radio frequency chains. Since only a few orthogonal beams are available and mission-driven UAV networks are autonomous and delay-sensitive, non-orthogonal multiple access (NOMA) over these beams is considered for agility and efficiency. In particular, aiming to address these challenges of highly dynamic mobility, we propose a machine learning framework to enable agile analog beam management for mmWave-NOMA transmissions. The proposed beam management aims to achieve efficient beam tracking and NOMA-grouping-aware analog beamforming optimization to facilitate mmWave-NOMA transmissions by judiciously utilizing angular domain information (ADI). More specifically, a Gaussian process machine learning-based ADI prediction scheme is proposed to track the angular dynamics of FUAVs, which facilitates fast beam-tracking for mmWave-NOMA transmissions. Moreover, by exploiting the predicted ADI, an unsupervised-learning-based FUAV grouping scheme is proposed to facilitate mmWave-NOMA transmissions with high radio-frequency chain efficiency, while a deep learning-based NOMA-grouping-aware fast transmit beamforming optimization scheme is proposed to improve the coverage of mmWave-NOMA transmissions in highly dynamic multi-UAVs networks. Simulation results validate the performance advantages of our proposed beam management scheme against state-of-the-art schemes.

Low-cost lightweight-client twin-field quantum key distribution network with wavelength division multiplexing

Abstract: Abstract. In recent years, quantum communication technology has gradually entered the practical stage. Multiple quantum key distribution (QKD) networks have been built and tested all over the world. However, quantum communication networking mode always evolves from a point-to-point network to a many-to-many network. The major challenges in quantum communication are secret key rate, distance, cost, and size of QKD devices. We proposed a low-cost, lightweight-client, twin-field QKD network with wavelength division multiplexing (WDM). The proposed implementation adopts polarization multiplexing, time-division multiplexing, and WDM technology to solve all these major challenges and implement a many-to-many dynamic communications network. Furthermore, our scheme can effectively reduce the cost to less than half of that of current QKD networks and has reference significance for large-scale deployment of QKD networks.

Research on Lightweight Blockchain Technology Based on Edge Computing

Abstract: With the continuous development of Internet of Things technology, It faces many security and trustworthiness issues. Blockchain technology has the characteristics of secure communication, tamper-proof, multi-party consensus and so on. Much research focused on the integration of blockchain and IoT. However, the traditional blockchain for IoT is based on cloud computing architecture, which has shortcomings such as prolonged communication time, slow blockchain processing speed, and difficulty in real-time uploading of massive IoT data to the blockchain. On the other hand, the traditional edge computing-based blockchain for IoT system only collects IoT data on the edge side, and the actual blockchain interaction work is still implemented in the cloud. This causes problems of data loss and high pressure on the central server. Against these problems, this paper proposes a lightweight and high-performance blockchain dedicated to IoT scenarios: Fast and Lightweight Blockchain for IoT. The proposed lightweight blockchain improves the speed of storage of IoT data. Experiments indicate the Transaction Per Second of FLBC-IoT far exceeds that of traditional blockchains such as Ethereum and Hyperledger Fabric. The proposed lightweight blockchain is suitable for IoT system based on edge computing.

Non-Orthogonal DMT Enabled by Iterative ICI Cancellation Algorithm for Bandwidth-Limited IM/DD Optical Systems

Abstract: In this paper, non-orthogonal discrete multi-tone (NO-DMT) is proposed for improving the performance of bandwidth-limited intensity-modulation and direct-detection (IM/DD) optical systems. The bandwidth of NO-DMT signal can be compressed to alleviate high-frequency distortions. Meanwhile, the adaptive bit and power loading in the NO-DMT can make full use of the SNR on each subcarrier. However, inter-carrier interference (ICI) is induced by the bandwidth compression, which significantly degrades the performance of NO-DMT. Therefore, we propose an iterative ICI cancellation algorithm for the NO-DMT, which is applicable to the large number of subcarrier and high-order modulation. For verifying the feasibility of the proposed scheme, a 50 Gbit/s NO-DMT system is experimentally demonstrated using 10 G-class optics at center wavelength of 1370 nm. Due to the limited bandwidth of the devices and the large chromatic dispersion at the wavelength of 1370 nm, the overall 10 dB bandwidth of the system is only approximately 10 GHz. Owing to the robustness to high-frequency distortions, the NO-DMT has 3 dB higher receiver sensitivity compared to DMT at the BER of $1.0\times 10^{-2}$. Thanks to the full use of SNR, the adaptive bit and power loading in NO-DMT improves approximately 6 dB receiver sensitivity. In conclusion, NO-DMT has the potential for application in bandwidth-limited IM/DD optical systems.

Design and Implementation of Network Traffic Analysis System for Power Grid Metering System

Abstract: Aiming at the problems of terminal failure detection and opaque flow status of electric energy metering network, this paper designs and implements a flow analysis system, which is divided into three parts: data acquisition layer, data analysis layer and data display layer. Combined with traffic collection, traffic statistics, cluster analysis, whitelist detection methods to achieve network traffic transparency and abnormal detection, can help network administrators to analyze network activities, has important significance for the supervision and safe operation of electric energy metering network.

A Hybrid Routing Mutation Mechanism based on Mutation Cost and Resource Trustworthiness in Network Moving Target Defense

Abstract: Network moving target defense technology can effectively defend against attacker monitoring of the service. The technology makes it more difficult for attackers to attack and can ensure secure communication for services. However, there may be multiple network attacks in the network. As a result, there may be security problems with the trustworthiness of the network resource mutation space required for network moving target defense technology. The focus of current researches is mostly on the mutation timing and cost optimization of the resource mutation space. It may lead to the possibility that the resources in the resource mutation space have been already under network attacks. In this paper, we propose a hybrid routing mutation mechanism based on mutation cost and resource trustworthiness in SDN networks. The mechanism is able to evaluate the routing mutation space selected by the routing-based network moving target defense technology. In terms of both mutation cost and resource trustworthiness, the optimal weighting ratio of the two factors will be to discovered. Simulation results show that the proposed mechanism can effectively enhance the ability of the routing mutation space to avoid attacked nodes and links and improve the transmission success rate of services.

Privacy-preserving Trajectory Generation Algorithm Considering Utility based on Semantic Similarity Awareness

Abstract: Location-based service recommendations usually need to collect and analyze the location information of trajectories generated by users with smart phones or wearable devices. It is easy to cause the location privacy leaks. The current location privacy protection methods usually confuse the adversary by adding fake locations into real trajectories to achieve the goal of privacy protection. However, these methods fail to consider the utility of user trajectories in service recommendations. In this paper, we propose a privacy-preserving trajectory generation algorithm based on service semantic similarity. To improve the utility of privacy-preserving trajectories for service recommendations, the algorithm constructs a series of service semantic grid maps and generates fake individuals with privacy-preserving trajectories considering both utility and privacy. Simulation results show that the proposed algorithm can effectively hide the locations in real trajectories of individuals and obtain higher effectiveness for service recommendations.

Experimental Demonstration of 2D-Trellis Coded Modulation PAM8 for 50G PON Based on 10G-class O-Band DML

Abstract: We present the first experimental demonstration of using 2D-TCM-PAM8 for a 50Gb/s PON system based on 10G-class O-band DML. Compared to PAM6, the receiver sensitivity of 2D-TCM-PAM8 is improved by 1.1 dB over 20-km transmission.

Access Control Mechanism for Edge Nodes

Abstract: Aiming at the problems of poor security, difficult data access control and inflexible authorization process of edge nodes in the Internet of Things environment, an access control mechanism for edge nodes is proposed. This mechanism builds a blockchain-based IoT data security and trusted sharing framework, deploys smart contracts for dynamic access control. The dynamic token mechanism is used to restrict access to edge node resources, and a variable token value is set to periodically generate access credentials for user login. The user’s access information is recorded on the blockchain, the reputation is evaluated according to the access information stored on the chain, and the resource access level is dynamically updated on a regular basis. Finally, deploy IoT edge nodes and build a fabric-based dynamic access control framework to conduct simulation and comparison experiments., which proves the feasibility and security of the access control scheme for edge nodes.

HPoC: A Lightweight Blockchain Consensus Design for the IoT

Abstract: The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabilities to overcome the bandwidth delay and service-congestion problems caused by excessive concentration in existing scenarios, but they face the challenges of limited computing, storage, and communication resources. As a result, a non- financial lightweight blockchain consensus design with low energy consumption, low latency, and greater stability should be investigated. We propose a hierarchical proof-of-capability (HPoC) consensus mechanism combined with the asynchronous proof-of-work (PoW) mechanism for improving the computing capacity, storage capacity, and communication capacity of IoT edge devices that can generate blocks with low latency, low power consumption, and strong stability in resource-constrained edge device nodes, while ensuring that the security of the edge devices is enhanced asynchronously. We simulated a smart-home scenario, with the number of device nodes ranging from 15 to 75, and conducted comparative experiments between HPoC and PoW based on different difficulty bits. The experimental results showed that HPoC is a consensus mechanism with scalability and stability that can flexibly adjust time consumption and accurately select nodes with strong capabilities to generate blocks in heterogeneous devices.

Heterogeneity-Aware Federated Learning for Device Anomaly Detection in Industrial loT

Abstract: With the popularity and application of the Industrial Internet of Things (1IoT), device anomaly detection is considered as one of the important challenges in IloT implementation. However, the privacy sensitivity of device data and the high heterogeneity of IloT devices make it impossible for traditional schemes to achieve efficient, accurate, and privacy-protected device anomaly detection in IloT networks. In this study, we propose an intelligent anomaly detection architecture for IloT networks based on federated optimization algorithms and deep learning (DL). In particular, an online, adaptive, and semi-supervised device anomaly detection model is designed, and a heterogeneity-aware federated learning algorithm, called Clustered-FedProx, is presented. The Clustered-FedProx algorithm considers the differences in computational power and data statistical distribution among IloT devices, whereby multiple devices can be coordinated to train a global DL model in highly heterogeneous networks. Simulation results show that the proposed scheme can achieve more stable and accurate performance than conventional schemes.

Security Evaluation Model of Blockchain System Based on Combination Weighting and Grey Clustering

Abstract: With the widespread application of blockchain technology in social and economic fields, various security challenges have become increasingly prominent and attracted many researchers to work in this domain. To address this issue, we propose a blockchain system security evaluation model based on game theory combination weighting and grey clustering. Considering the correlation between indexes, the security evaluation index system is constructed hierarchically from the technical system framework of blockchain. Then we use the combination weighting algorithm based on game theory to optimize the ratio of subjective and objective weights, and balance the evaluation error caused by the difference between subjective and objective weights to make the weight quantification more scientific and accurate. Finally, the security evaluation level of the blockchain system is determined by grey clustering evaluation method. The effectiveness of this method is verified by our experiments, which provides a useful reference for the security evaluation of blockchain systems.

Latency-Efficient Parallelized Carrier Phase Recovery Algorithm Based on Extended Kalman Filter for Short-Reach Coherent Optical Transmission Systems

Abstract: In this paper, a latency-efficient parallelized carrier phase recovery algorithm based on the extended Kalman filter with linear interpolation (LEP-EKF) is proposed and experimentally verified for short-reach coherent optical transmission systems. Compared with the traditional parallelized extended Kalman filter (EKF), the processing latency of LEP-EKF is reduced by improving the parallelism of each data block. Moreover, the linewidth tolerance of LEP-EKF has almost no penalty because the linear interpolation is used to quick convergence of each data block without extra overhead. The simulation results show that compared with the traditional parallelized EKF, the processing latency of LEP-EKF is reduced by 75% at the similar linewidth tolerance of $\Delta {\mathcal{f}}{\mathbf{T}}=9\times10^{-5}$. The LEP-EKF is further experimentally demonstrated under the linewidth symbol duration product of $\Delta fT=5\times 10^{-5}$. The experimental results show the processing latency of LEP-EKF also saves 75% with little performance penalty. In conclusion, the LEP-EKF is a potential technique in short-reach coherent optical transmission systems.

Progressive Evolution Scheme with Socialization Swarm for Privacy Blockchain

Abstract: To enhance the dependability of lightweight blockchain, this paper presents a privacy protection mechanism in open networks with a secure ledger for each cell of society. We propose a swarm intelligence scheme called hierarchical RAFT (HRAFT), which accumulates valuable workloads about the credibility of all the nodes to sort for selecting candidates. Thus, the scheme achieves high-performance decentralization by progressive evolution consensus.

Privacy Protection Management Model for Internet of Things Data

Abstract: With the rapid development of the Internet of Things, personal information is collected, stored and analyzed through IoT terminal devices, which is benefit for people’s daily life. However, the problems of personal private information leakage are also emerging. In terms of ciphertext data access control of the Internet of Things, it is required to implement fine-grained access control policies for the authorizer. However, the traditional security channel cannot meet the requirements of application security and privacy protection based on resisting key sharing attacks in a “many to many” environment. To solve this problem, this paper proposes a privacy protection management model based on lightweight blockchain, which includes three modules: data storage, sharing and auditing. Data storage module is used to encrypt and store the private data generated by individual users in various IoT devices. The data sharing module proposes a hybrid encryption mechanism, which allows data owners to create data access policies, and uses attribute encryption algorithm to achieve fine-grained access control of private data. The data auditing module proposes a lightweight blockchain architecture to store root of trust and life-cycle behaviors of private data, so as to realize the integrity verification and traceability of circulation trajectory. Finally, the security analysis of the proposed model can resist wireless communication eavesdropping and tampering attacks, attribute inference attack and identity impersonation attack. Simulation experiments are carried out on Fabric and lightweight blockchain systems to compare and analyze the performance consumption of calculating the root of trust, blockchain storage and query. The experimental results show that the performance of the proposed lightweight blockchain is 1666 TPS, which can meet the batch certificate storage requirements in the Internet of Things scenario.

A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis

Abstract: Recognizing the type of connected devices to a network helps to enforce security policies. In smart grids, identifying massive number of grid metering terminals based on network traffic analysis is almost blank and existing research has not proposed a targeted end-to-end model to solve the flow classification problem. Therefore, we proposed a hierarchical terminal recognition approach that applies the details of grid data. We have formed a two-level model structure by segmenting the grid data, which uses the statistical characteristics of network traffic and the specific behavior characteristics of grid metering terminals. Moreover, through the selection and reconstruction of features, we combine three algorithms to achieve accurate identification of terminal types that transmit network traffic. We conduct extensive experiments on a real dataset containing three types of grid metering terminals, and the results show that our research has improved performance compared to common recognition models. The combination of an autoencoder, K-Means and GradientBoost algorithm achieved the best recognition rate with F1 value of 98.3%.

Interaction of Probabilistic Shaping and the LDPC Code Rate

Abstract: We evaluated the interaction of probabilistic shaping and forward error correction using low-density parity check. The results indicate that the shaping and the code rate should be simultaneously taken into consideration in probabilistic shaping system.