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SSRN 2023年

作者： Cui, Bo Hu, Yun Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China College of Computer Science Inner Mongolia University Hohhot China

Blockchain technology has found widespread use in various fields, including finance, healthcare, the Internet of Things, and supply chain management, due to its ability to meet technical and non-technical requirements. Therefore, it is crucial for system designers to evaluate the performance and characteristics of a given blockchain platform before deploying it. One way to achieve this is through the use of blockchain simulators. However, existing simulators have limitations in areas such as model scaling, event scheduling, time control, object model evaluation, and simulation magnitude. To address these limitations, we propose a Blockchain Simulator based on Event-Layered Architecture (BSELA) in this paper. BSELA comprises multiple functional modules with effective model scalability, with the core being our proposed Event-Layered Architecture. This architecture introduces a new event-driven scheduling mechanism for discrete-event simulation, which improves the efficiency, stability, and maintainability of event scheduling during blockchain simulation. Furthermore, we propose a time advancement mechanism for subsequent events based on event rounds, which improves the accuracy of time advancement, simulation complexity, and real-time demand handling capability during the simulation. We begin by validating the efficiency of our simulator by comparing experiments with those of existing blockchain simulators. We then validate the accuracy of BSELA through simulation experiments on blockchain network connectivity, block propagation latency, and the number of INV messages propagated per hour. To verify the accuracy of our results, we compare them with historical Bitcoin data. Finally, we design and experimentally validate a node trust network construction mechanism by tuning the simulator blockchain object model. Our experiments show that this tuning mechanism improves data transfer efficiency by 30-40%, and also improves security. The design and experiments of the BSELA

关键词： Blockchain

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Deep Learning for Regional Subsidence Crisis Prediction in Smart Grid Infrastructure

Deep Learning for Regional Subsidence Crisis Prediction in S...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Zhaoran Wang Xiangyu Bai Yu Han Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

Power infrastructure and its connectivity are central to building a smart grid. The infrastructure regional subsidence caused by environmental factors or geological hazards may devastate grid systems. Therefore, it is critical to forecasting the infrastructure regional subsidence in smart grids. In this study, we used an InSAR time series subsidence dataset based on satellite remote sensing images to train, test and compare four deep learning-based Transformer series prediction models using transfer learning to achieve subsidence crisis monitoring and prediction in smart grid infrastructure areas, considering the influence of environmental factors on infrastructure regional subsidence. Meanwhile, an GIS for subsidence crisis prediction in smart grid infrastructure areas was developed based on the Autoformer model. It helps the power industry maintain the smart grid more efficiently and accurately while also saving a lot of money and labor.

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Node Importance Algorithm of Opportunistic Network Based on Social Properties of Nodes

Node Importance Algorithm of Opportunistic Network Based on ...

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International Conference on computer Supported Cooperative Work in Design

作者： Gang Xu Xinyu Hao Zhihan Qi Baoqi Huang College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot China

Recent research on opportunistic network routing protocols has found that the efficiency of message forwarding varies with the selection of next-hop nodes, so it is of great significance to select the appropriate next- hop nodes for message forwarding. This paper proposes a Node Importance Algorithm Based on Social Properties of Nodes (NI). The importance evaluation model is established by using the social attributes of node centrality and relevance, and the importance forwarding algorithm is constructed according to this model. The algorithm first calculates the node importance and selects the node with higher importance as the next hop forwarding node based on this model. The experimental simulation results show that the algorithm can select more appropriate next-hop nodes to complete message forwarding, so as to improve the message delivery rate of the network.

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Research on data collection mechanism of wireless sensor network based on UAV 21

Research on data collection mechanism of wireless sensor net...

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2nd International Conference on computing, Networks and Internet of Things, CNIOT 2021

作者： Xie, Wen Bai, Xiangyu Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Collece of Computer Science Inner Mongolia University China

ISBN: (纸本)9781450389693

In recent years, wireless Sensor networks (WSNs) have developed rapidly and have been widely used in various fields such as commerce, agriculture, military affairs and the environment. However, with the development of technology and the increasing demand of application scenarios, the limitations of WSNs are becoming more and more obvious. On the other hand, through continuous improvement, Unmanned Aerial Vehicle (UAV) technology has become more and more powerful, which can provide reliable, cost-effective services for people. The connectivity and energy efficiency of WSN can be significantly improved by taking advantage of UAV's high altitude hovering capability, communication capability and flexible mobility. Many researchers believe that UAV assisted communication in WSN is a promising solution. In this paper, we first discuss the limitations of WSN in certain scenarios and the advantages of UAV-assisted WSN. Secondly, the development and research status of UAV-assisted wireless sensor network communication are introduced. In this process, this paper introduces the research status of three data collection methods: data storage and forwarding UAV, mobile relay UAV and aerial base station UAV, and discusses the research progress in the field of wireless communication of UAV. Finally, this paper compares and analyzes the above three types of UAV-assisted sensor network data collection methods, and prospects the future research of UAV-assisted WSN. © 2021 ACM.

关键词： Antennas

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Enhancing AoA Estimation Via Phase Modeling of Bluetooth 5 CTE Signals

Enhancing AoA Estimation Via Phase Modeling of Bluetooth 5 C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wentao Shi Tao Zhang Baoqi Huang Bing Jia Ministry of Education Engineering Research Center of Ecological Big Data China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

Angle of Arrival (AoA) estimation based on Constant Tone Extension (CTE) signals in Bluetooth 5 suffers from carrier frequency offsets (CFO) arising from the carrier frequency mismatch between the receiver and the transmitter, extra noises coming from switching frequencies when sampling CTE signals, as well as the phase folding problem due to the usage of mod2π in mapping the increasing phase values in estimation models into [0, 2π). To address these issues, a generalized phase model involving the CFO compensation and the time offset is established, and an adaptive gradient descent optimization algorithm based on the real sampling mechanism is proposed to solve the phase folding problem efficiently while obtaining high-precision AoA estimation. Experimental results show that our method reduces the average estimation error by at least 10% in outdoor scenes and 61% in indoor scenes compared to the conventional methods.

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SCRN: A Spectrogram Convolutional Recurrent Network for AoA Estimation Using Bluetooth 5

SCRN: A Spectrogram Convolutional Recurrent Network for AoA ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wentao Shi Baoqi Huang Bing Jia Engineering Research Center of Ecological Big Data Ministry of Education China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

Bluetooth 5 employs Constant Tone Extension (CTE) signals to simulate arrival times of a common signal at multiple antennas, but incurs limited accuracy due to inevitable frequency offsets caused by Gaussian frequency shift keying (GFSK) and time offsets caused by antenna switching. To address this issue, this paper proposes SCRN, an efficient Bluetooth 5 Angle of Arrival(AoA) estimation scheme that combines spectrogram analysis with deep learning co-design. SCRN leverages neural networks to generate a super-resolution spectrogram, effectively eliminating frequency offsets and recovering the true underlying frequency. Subsequently, it utilizes a lightweight convolutional recurrent network to encode the input features into a high-dimensional latent space, so as to alleviate the effect of time offsets. Through extensive experiments conducted in practical indoor settings, the proposed scheme achieves an AoA estimation error of only 1.3 degrees, surpassing traditional algorithms by over 90%.

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Opportunistic Network Performance Optimization Model Based on a Combination of Neural Networks and Orthogonal Experiments 7th

Opportunistic Network Performance Optimization Model Based o...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Qi, Zhihan Pan, YunChao Du, Hao Zhang, Na Bai, ZongHan Xu, Gang College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot China

ISBN: (纸本)9783030786113

Under the complex environmental conditions of remote geographic locations and fragmented network areas, sending data over traditional wired networks or wireless self. Organizing networks often results in connection interruptions, packet loss and other problems. In order to reduce the problem of data loss due to network disruptions, researchers have proposed a new network technology for cut networks, named opportunity Network. Opportunity network messages have a high rate of packet loss due to factors such as the small probability of node encounters and message lifetime limits. In order to reduce the packet loss rate, improve the success rate of message transmission and reduce the message transmission delay, the paper proposes a node message transmission performance optimization model based on the fusion of neural networks and orthogonal experiments. The experimental results show that the prediction model proposed in the paper for optimizing node message transmission performance can predict the packet loss rate more accurately, and it is found that node density, node rate, and node cache are the most important factors influencing the message packet loss rate of the opportunity network, and the optimal settings of these three factors are obtained to reduce the packet loss rate of message transmission and improve the message delivery efficiency. © 2021, Springer Nature Switzerland AG.

关键词： Packet loss

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Detecting Phishing Scams on Ethereum Using Graph Convolutional Networks with Conditional Random Field

Detecting Phishing Scams on Ethereum Using Graph Convolution...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Wenhan Hou Bo Cui Ru Li Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

As an innovative technology of great significance, blockchain has been widely used in various walks of life. Meanwhile, scams have experienced rapid growth on the blockchain, the phishing scam is a classic fraud among them. Ethereum is the largest blockchain platform that supports smart contracts, and its ecosystem has been under serious threat due to phishing scams. Consequently, phishing scam detection is particularly critical for Ethereum to avoid economic loss. However, accounts on Ethereum are represented as strings without features, which brings great difficulties for detection. To combat this challenge, we propose an effective method based on Graph Convolutional Network (GCN) with Conditional Random Field (CRF) to detect phishing scams. Firstly, we process accounts and their neighbors with transaction records to build transaction graphs. Due to lack of portrait information, we adopt DeepWalk to provide initial features for each node. Then graph representations are learned through GCN with CRF. The extensive experiments show that the proposed model performs better than comparison methods on both precision and recall, which indicates that our method can effectively identify phishing scams on Ethereum.

关键词： Economics Phishing Smart contracts Ecosystems Feature extraction Blockchains Fraud

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A Dynamic Load Balancing Scheme Based on Network Sharding in Private Ethereum Blockchain

A Dynamic Load Balancing Scheme Based on Network Sharding in...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Zicheng Wang Bo Cui Wenhan Hou Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Applications in the private Ethereum blockchain environment are required by many enterprises. Although, un-like public Ethereum platforms, private Ethereum platforms allow some specific parameters to be configured at setup time to increase throughput, its throughput performance is still not sufficient for enterprise-level applications due to the low scalability of the Ethereum blockchain. This paper analyzes and tests the throughput of the private Ethereum blockchain at the hardware level, and according to the analysis results, proposes a parallelized dynamic load balancing scheme based on network sharding. On this basis, various charts are created to visually show the impact of changing hardware parameters on performance, and experiments are conducted to verify that the model can further improve the performance of private Ethereum blockchains through effective load balancing.

关键词： Scalability Conferences Load management Throughput Hardware Software Blockchains

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Counting mobile Devices With Physical Layer Measurements in the 2.4GHz ISM Band 19

Counting Mobile Devices With Physical Layer Measurements in ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhang, Zhaoyan Huang, Baoqi Zhang, Chaowei Jia, Bing Li, Wuyungerile Xu, Gang Engineering Research Center of Ecological Big Data Ministry of Education China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China Inner Mongolia University College of Computer Science Hohhot China Inner Mongolia Power Marketing Service and Operation Management Center China

ISBN: (纸本)9798350346558

In general, the more mobile devices are operating in space, the more wireless communications are produced, leading to significant changes in the wireless spectrum, which can be utilized to infer the number of mobile devices conversely. Inspired by this idea, we pioneered present a novel mobile device counting method of measuring the wireless signals of the 2. 4GHz ISM band in which most WiFi-and Bluetooth-enabled mobile devices operate. Precisely, the in-phase (I) and quadrature (Q) components from the wireless signals are measured through a software-defined radio (SDR) platform and are then converted into spectrograms. On this ground, we propose a dual attention network based on EfficientNet-B4 (EANet) to estimate the number of mobile devices with the aforementioned spectrograms as inputs. In addition, a cross-domain estimation method is proposed to enhance the generalizability of our model. Experiments are conducted in real environments to validate our method. It is shown that the proposed regression model EANet outperforms the models based on popular convolutional neural networks (CNNs), and the proposed cross-domain method can effectively reduce the training time without sacrificing the estimation accuracy. This study preliminarily verifies the idea's feasibility and the validity of the proposed methods, paving the way for future research. © 2022 IEEE.

关键词： Network layers

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