arXiv subjects

Hongfang Yu

Publications and source records attributed to Hongfang Yu.

At least 37 records · Page 2Linked to original sources

arXiv2024

User Association and Channel Allocation in 5G Mobile Asymmetric Multi-band Heterogeneous Networks

With the proliferation of mobile terminals and the continuous upgrading of services, 4G LTE networks are showing signs of weakness. To enhance the capacity of wireless networks, millimeter waves are introduced to drive the evolution of networks towards multi-band 5G heterogeneous networks. The distinct propagation characteristics of mmWaves and microwaves, as well as the vastly different hardware configurations of heterogeneous base stations, make traditional access strategies no longer effective. Therefore, to narrowing the gap between theory and practice, we investigate the access strategy in multi-band 5G heterogeneous networks, taking into account the characteristics of mobile users, asynchronous switching between uplink and downlink of pico base stations, asymmetric service requirements, and user communication continuity. We formulate the problem as integer nonlinear programming and prove its intractability. Thereby, we decouple it into three subproblems: user association, switch point selection, and subchannel allocation, and design an algorithm based on optimal matching and spectral clustering to solve it efficiently. The simulation results show that the proposed algorithm outperforms the comparison methods in terms of overall data rate, effective data rate, and number of satisfied users.

cs.NI↗

arXiv2024

Accelerating Geo-distributed Machine Learning with Network-Aware Adaptive Tree and Auxiliary Route

Distributed machine learning is becoming increasingly popular for geo-distributed data analytics, facilitating the collaborative analysis of data scattered across data centers in different regions. This paradigm eliminates the need for centralizing sensitive raw data in one location but faces the significant challenge of high parameter synchronization delays, which stems from the constraints of bandwidth-limited, heterogeneous, and fluctuating wide-area networks. Prior research has focused on optimizing the synchronization topology, evolving from starlike to tree-based structures. However, these solutions typically depend on regular tree structures and lack an adequate topology metric, resulting in limited improvements. This paper proposes NetStorm, an adaptive and highly efficient communication scheduler designed to speed up parameter synchronization across geo-distributed data centers. First, it establishes an effective metric for optimizing a multi-root FAPT synchronization topology. Second, a network awareness module is developed to acquire network knowledge, aiding in topology decisions. Third, a multipath auxiliary transmission mechanism is introduced to enhance network awareness and facilitate multipath transmissions. Lastly, we design policy consistency protocols to guarantee seamless updates of transmission policies. Empirical results demonstrate that NetStorm significantly outperforms distributed training systems like MXNET, MLNET, and TSEngine, with a speedup of 6.5~9.2 times over MXNET.

cs.DC↗

arXiv2024

Blockchain for Energy Market: A Comprehensive Survey

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the energy market.

cs.NI↗

arXiv2024

A Survey of Network Protocol Fuzzing: Model, Techniques and Directions

As one of the most successful and effective software testing techniques in recent years, fuzz testing has uncovered numerous bugs and vulnerabilities in modern software, including network protocol software. In contrast to other fuzzing targets, network protocol software exhibits its distinct characteristics and challenges, introducing a plethora of research questions that need to be addressed in the design and implementation of network protocol fuzzers. While some research work has evaluated and systematized the knowledge of general fuzzing techniques at a high level, there is a lack of similar analysis and summarization for fuzzing research specific to network protocols. This paper offers a comprehensive exposition of network protocol software's fuzzing-related features and conducts a systematic review of some representative advancements in network protocol fuzzing since its inception. We summarize state-of-the-art strategies and solutions in various aspects, propose a unified protocol fuzzing process model, and introduce the techniques involved in each stage of the model. At the same time, this paper also summarizes the promising research directions in the landscape of protocol fuzzing to foster exploration within the community for more efficient and intelligent modern network protocol fuzzing techniques.

cs.NI↗

arXiv2023

Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels

With the rapid growth of edge intelligence, the deployment of federated learning (FL) over wireless networks has garnered increasing attention, which is called Federated Edge Learning (FEEL). In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trained models. Moreover, devices can engage in decentralized FL via Device-to-Device communication while the communication topology of connected devices also impacts the generalization of models. Most recent theoretical studies overlook the incorporation of all these effects into FEEL when developing generalization analyses. In contrast, our work presents an information-theoretic generalization analysis for topology-aware FEEL in the presence of data heterogeneity and noisy channels. Additionally, we propose a novel regularization method called Federated Global Mutual Information Reduction (FedGMIR) to enhance the performance of models based on our analysis. Numerical results validate our theoretical findings and provide evidence for the effectiveness of the proposed method.

cs.IT↗

arXiv2023

Symbiotic Blockchain Consensus: Cognitive Backscatter Communications-enabled Wireless Blockchain Consensus

The wireless blockchain network (WBN) concept, born from the blockchain deployed in wireless networks, has appealed to many network scenarios. Blockchain consensus mechanisms (CMs) are key to enabling nodes in a wireless network to achieve consistency without any trusted entity. However, consensus reliability will be seriously affected by the instability of communication links in wireless networks. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. Energy-intensive blockchain functions can quickly drain the power of nodes, thus degrading consensus performance. Fortunately, a symbiotic radio (SR) system enabled by cognitive backscatter communications can solve the above problems. In SR, the secondary transmitter (STx) transmits messages over the radio frequency (RF) signal emitted from a primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. Such an approach is useful for almost all vote-based CMs, such as the Practical Byzantine Fault-tolerant (PBFT)-like and the RAFT-like CMs. This paper proposes symbiotic blockchain consensus (SBC) by transforming 6 PBFT-like and 4 RAFT-like state-of-the-art (SOTA) CMs to demonstrate universality. These new CMs will benefit from mutualistic transmission relationships in SR, making full use of the limited spectrum resources in WBN. Simulation results show that SBC can increase the consensus success rate of PBFT-like and RAFT-like by 54.1% and 5.8%, respectively, and reduce energy consumption by 9.2% and 23.7%, respectively.

cs.NI↗

arXiv2023

Symbiotic PBFT Consensus: Cognitive Backscatter Communications-enabled Wireless PBFT Consensus

Wireless blockchain networks have played an important role in many network scenarios, among which wireless Practical Byzantine Fault Tolerance (PBFT) consensus is regarded as one of the most important consensus mechanisms. It enables nodes in wireless networks to reach consistency without any trusted entity. However, due to the instability of wireless communication links, the reliability of the PBFT consensus will be seriously affected. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. The high-energy-consumption blockchain functions will quickly consume the power of nodes, thus, affecting consensus performance. Fortunately, the symbiotic radio (SR) system enabled by cognitive backscatter communications can provide a solution to the above problems. In SR, the secondary transmitter (STx) transmits messages by modulating its information over the radio frequency (RF) signal of the primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. In our paper, we propose the symbiotic PBFT (S-PBFT) consensus benefited from the mutualistic transmission in SR, which can increase the consensus security by 54.82%, and save energy consumption by about 10%.

cs.NI↗

arXiv2023

Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study the performance of non-ideal wireless PBFT and RAFT networks with THz and mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this paper, we study and compare the performance of THz and mmWave signals in non-ideal wireless PBFT and RAFT networks, considering Rayleigh Fading (RF) and close-in Free Space (FS) reference distance path loss. Performance is evaluated by five metrics: consensus success rate, latency, throughput, reliability gain, and energy consumption. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make CMs inevitably successful, and it is named the active distance of CMs. The research results analyze the performance of non-ideal wireless PBFT and RAFT networks, and provide important references for the future transmission of THz and mmWave signals in PBFT and RAFT networks.

cs.NI↗

arXiv2023

ESCM: An Efficient and Secure Communication Mechanism for UAV Networks

UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network. As high-speed moving objects, UAVs not only have extremely strict requirements for communication delay, but also cannot be maliciously controlled as a weapon by the attacker. Therefore, an efficient and secure communication method designed for UAV networks is necessary. We propose a communication mechanism ESCM. For high efficiency, ESCM provides a routing protocol based on the artificial bee colony (ABC) algorithm to accelerate communications between UAVs. Meanwhile, we use blockchain to guarantee the security of UAV networks. However, blockchain has unstable links in high-mobility networks resulting in low consensus efficiency and high communication overhead. Consequently, ESCM introduces digital twin (DT), which transforms the UAV network into a static network by mapping UAVs from the physical world into Cyberspace. This virtual UAV network is called CyberUAV. Then, in CyberUAV, we design a blockchain consensus based on network coding, named Proof of Network Coding (PoNC). Analysis and simulation show that the above modules in ESCM have advantages over existing schemes. Through ablation studies, we demonstrate that these modules are indispensable for efficient and secure communication of UAV networks.

cs.NI↗

arXiv2023

ESIA: An Efficient and Stable Identity Authentication for Internet of Vehicles

Decentralized, tamper-proof blockchain is regarded as a solution to a challenging authentication issue in the Internet of Vehicles (IoVs). However, the consensus time and communication overhead of blockchain increase significantly as the number of vehicles connected to the blockchain. To address this issue, vehicular fog computing has been introduced to improve efficiency. However, existing studies ignore several key factors such as the number of vehicles in the fog computing system, which can impact the consensus communication overhead. Meanwhile, there is no comprehensive study on the stability of vehicular fog composition. The vehicle movement will lead to dynamic changes in fog. If the composition of vehicular fog is unstable, the blockchain formed by this fog computing system will be unstable, which can affect the consensus efficiency. With the above considerations, we propose an efficient and stable identity authentication (ESIA) empowered by hierarchical blockchain and fog computing. By grouping vehicles efficiently, ESIA has low communication complexity and achieves high stability. Moreover, to enhance the consensus security of the hierarchical blockchain, the consensus process is from the bottom layer to the up layer (bottom-up), which we call B2UHChain. Through theoretical analysis and simulation verification, our scheme achieves the design goals of high efficiency and stability while significantly improving the IoV scalability to the power of 1.5 (^1.5) under similar security to a single-layer blockchain. In addition, ESIA has less communication and computation overhead, lower latency, and higher throughput than other baseline authentication schemes.

cs.CR↗

arXiv2023

Maximize the Long-term Average Revenue of Network Slice Provider via Admission Control Among Heterogeneous Slices

Network slicing endows 5G/B5G with differentiated and customized capabilities to cope with the proliferation of diversified services, whereas limited physical network resources may not be able to support all service requests. Slice admission control is regarded as an essential means to ensure service quality and service isolation when the network is under burden. Herein, the scenario where rational tenants coexist with partially competitive network slice providers is adopted. We aim to maximize the long-term average revenue of the network operators through slice admission control, with the feasibility of multidimensional resource requirements, the priority differences among heterogeneous slices, and the admission fairness within each slice taken into account concurrently. We prove the intractability of our problem by a reduction from the Multidimensional Knapsack Problem (MKP), and propose a two-stage algorithm called MPSAC to make a sub-optimal solution efficiently. The principle of MPSAC is to split the original problem into two sub-problems; inter-slice decision-making and intra-slice quota allocation, which are solved using a heuristic method and a tailored auction mechanism respectively. Extensive simulations are carried out to demonstrate the efficacy of our algorithm, the results show that the long-term average revenue of ours is at least 9.6% higher than comparisons while maintaining better priority relations and achieving improved fairness performance.

cs.NI↗

arXiv2023

Performance Analysis of Non-ideal Wireless PBFT Networks with mmWave and Terahertz Signals

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) is seen as the most applicable consensus mechanism in blockchain-enabled wireless networks. However, previous studies on PBFT do not consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high frequency signals such as millimeter wave (mmWave) and terahertz (THz), while the performance of PBFT is still unknown when these signals are transmitted in wireless PBFT networks. Therefore, it is urgent to study the performance of non-ideal wireless PBFT networks with mmWave and THz siganls, so as to better make PBFT play a role in 6G era. In this paper, we study and compare the performance of mmWave and THz signals in non-ideal wireless PBFT networks, considering Rayleigh Fading (RF) and close-in Free Space (FS) reference distance path loss. Performance is evaluated by consensus success rate and delay. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make PBFT consensus inevitably successful, and it is named active distance of PBFT in this paper. The research results not only analyze the performance of non-ideal wireless PBFT networks, but also provide an important reference for the future transmission of mmWave and THz signals in PBFT networks.

cs.NI↗

arXiv2022

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a high-performance and efficient system named HFEDMS for incorporating practical FL into Industrial Metaverse. HFEDMS reduces data heterogeneity through dynamic grouping and training mode conversion (Dynamic Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters (Semantic Compression and Compensation, SCC). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies (Layer-wiseAlternative Synchronization Protocol, LASP) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show that HFEDMS improves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.

cs.LG↗

arXiv2022

Cross-Silo Heterogeneous Model Federated Multitask Learning

Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants utilizing cross-silo federated learning (CS-FL) settings are independent organizations with different task needs, and they are concerned not only with data privacy but also with independently training their unique models due to intellectual property considerations. Most existing FL methods are incapable of satisfying the above scenarios. In this study, we present a novel federated learning method CoFED based on unlabeled data pseudolabeling via a process known as cotraining. CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes. The experimental results suggest that the proposed method outperforms competing ones. This is especially true for non-independent and identically distributed settings and heterogeneous models, where the proposed method achieves a 35% performance improvement.

cs.LG↗

arXiv2022

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

cs.NI↗

arXiv2022

PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

5G/B5G is envisioned to support various services with the assistance of network slices, each slice instance asks for adequate resources to provide the pre-negotiated service quality to its subscribers. Slice Admission Control (SAC) algorithm is a necessity for Slice Providers (SPs) to guarantee the QoS and QoE of each admitted request with limited resources. In that circumstance, the priority concern of services and the fairness of resource allocation arise as meaningful topics for researchers. The former originates from the innate characteristics of various services supported by 5G networks, and the latter matters because slices are instantiated on shared physical equipment. However, the two issues are mainly investigated separately in the literature or do not receive sufficient research simultaneously. In this work, we study the SAC problem in 5G/B5G networks, aiming at enhancing the fairness degree on the premise of satisfying the necessary priority requirements. We first reinterpret priority as a higher cumulative service acceptance ratio (CSAR), and adopt the uniformity of adjacent CSAR gaps to reflect the fairness. Based on these adjustments, the SAC problem is formulated as a non-linear and non-convex multi-objective optimization. Thus, we propose a heuristic algorithm called Prioritized Slice Admission Control Considering Fairness (PSACCF) to solve it. It introduces the resource efficiency of services to amend priority violations, then promotes fairness by setting the target CSARs for each service type and pushing their actual CSARs toward. Numerous simulations are carried out to compare the performance of PSACCF with two existing algorithms, termed MHPF and AHPF. Results show that our algorithm can achieve a nearly identical priority indicator to the comparisons, as well as at least a 33.6% improvement in fairness degree and a higher minimum average resource utilization.

cs.NI↗

arXiv2022

PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks

Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent IoT applications while protecting client data privacy and security. Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot. Most personalized federated learning research, however, focuses on data heterogeneity while ignoring the need for model architecture heterogeneity. Most existing federated learning methods uniformly set the model architecture of all clients participating in federated learning, which is inconvenient for each client's individual model and local data distribution requirements, and also increases the risk of client model leakage. This paper proposes a federated learning method based on co-training and generative adversarial networks(GANs) that allows each client to design its own model to participate in federated learning training independently without sharing any model architecture or parameter information with other clients or a center. In our experiments, the proposed method outperforms the existing methods in mean test accuracy by 42% when the client's model architecture and data distribution vary significantly.

cs.LG↗

arXiv2022

SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using Snapshots

Greybox fuzzing has been widely used in stateless programs and has achieved great success. However, most state-of-the-art greybox fuzzers generally have the problems of slow speed and shallow state depth coverage in the process of fuzzing stateful network protocol programs which are able to remember and store details of the interactions. The existing greybox fuzzers for network protocol programs send a series of well-defined prefix sequences of input messages first and then send mutated messages to test the target state of a stateful network protocol. The process mentioned above causes a high time cost. In this paper, we propose SNPSFuzzer, a fast greybox fuzzer for stateful network protocol using snapshots. SNPSFuzzer dumps the context information when the network protocol program is under a specific state and restores it when the state needs to be fuzzed. Furthermore, we design a message chain analysis algorithm to explore more and deeper network protocol states. Our evaluation shows that, compared with the state-of-the-art network protocol greybox fuzzer AFLNET, SNPSFuzzer increases the speed of network protocol fuzzing by 112.0%-168.9% and improves path coverage by 21.4%-27.5% within 24 hours. Moreover, SNPSFuzzer exposes a previously unreported vulnerability in program Tinydtls.

cs.CR↗