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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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Long-term Time Series Prediction of Deformation in The Area of Pylons By Combining InSAR and Transformer-based

Long-term Time Series Prediction of Deformation in The Area ...

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Frontiers of computer Science and Information Technology (FCSIT), Euro-Asia Conference on

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

Settlement monitoring of high-voltage line pylons (HLPs) is an important part of power line inspection. In geologically active areas, or due to some environmental factors, it can substantially impact the stability of HLP bases, which can lead to collapse accidents, so it is essential to monitor and predict the areas around HLPs. In this paper, we use 74-view Sentinel-1A satellite ascending orbit data, and combine the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) with a long-term sequence prediction model (Autoformer) to perform surface deformation of a high-voltage line in inner mongolia, China, from January 2019 to January 2022 monitoring. The environmental factors were considered to predict the surface deformation in the key study area from February 2022 to April 2022 and provide early warning. By comparing with Transformer, Informer, and Reformer, the results show that the proposed combined PS-InSAR and Autoformer method is far superior to its counterparts with a mean squared error of 0.105. This paper provides a new, accurate, and automated method for HLP subsidence monitoring and early warning and establishes a system for HLPs surface deformation prediction, which highly improves the efficiency of HLP monitoring and early warning.

关键词： Deformable models Deformation Time series analysis Poles and towers Predictive models Inspection Transformers

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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

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IEEE Transactions on mobile computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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Research on data collection and energy supplement mechanism in WRSN based on UAV: a method to maximize energy supplement efficiency

Research on data collection and energy supplement mechanism ...

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International Conference on mobile Ad-hoc and Sensor Networks, MSN

作者： Wen Xie Xiangyu Bai Yaru Ren Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing School of Computer Science Inner Mongolia University Hohhot China

Energy has always been a key bottleneck restricting the large-scale deployment and long-term operation of wireless sensor networks (WSNs). wireless rechargeable sensor networks (WRSNs) can effectively alleviate the energy-constrained problem of sensor nodes. However, due to the constraints of the service capabilities of mobile charging equipment, how to efficiently replenish energy and maintain the long-term operation of the network is still very challenging. In this paper, we propose an UAV-based energy replenishment mechanism in WRSN, which aims to maximize the replenished energy benefit of the network from the energy expended by UAVs. This paper firstly constructs the network model and defines the optimization problem of energy replenishment efficiency maximization. Then, in order to improve the performance of WRSN in terms of energy replenishment efficiency, the node clustering, anchor node selection and flight path planning problems are respectively studied, and a data collection and energy replenishment mechanism is proposed for the above problems. The experimental results show that the proposed scheme can effectively improve the energy replenishment efficiency of the system, prolong the life of the network and balance the energy consumption of the network.

关键词： wireless communication wireless sensor networks Energy consumption Analytical models Clustering algorithms Data collection Path planning

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Seed Node Selection Algorithm Based on Node Influence in Opportunistic Offloading

Seed Node Selection Algorithm Based on Node Influence in Opp...

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IEEE Symposium on computers and Communications (ISCC)

作者： Qi Tang Ruijie Hang Gang Xu Gaofeng Zhang Shuai Li 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 Ordos dongsheng manshi middle school Ordos China

As a kind of mobile traffic offloading technology, opportunistic triage downloads and distributes data through seed nodes. Therefore, how to efficiently and accurately select suitable seed nodes becomes a key problem of opportunistic streaming technology. To address the problem of insufficient research on seed node content coverage in existing studies, this paper combines the characteristics of opportunity networks and the solution idea of the influence maximization problem and proposes the evaluation model of node influence for the first time. Then proposes a seed node selection algorithm (SNSNI) based on node influence on this basis. Experimental results show that the set of seed nodes selected by SNSNI algorithm can obtain smaller average message transmission delay and more covered nodes in the triage scenario compared with the random algorithm, and fewer seed nodes are required to achieve the same effect.

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A Routing Algorithm based on Social Closeness and Spatio-Temporal Interaction Degree

A Routing Algorithm based on Social Closeness and Spatio-Tem...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Zhanguo Liu Gang Xu Ruijie Hang Yuan Cheng Baoqi Huang Xiangyu Bai College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

In sparse opportunistic networks with social at-tributes and limited cache space, routing algorithms based on long-term and stable social relations have low message delivery rates in a short period of time, and do not consider the impact of delivered message copies on communication efficiency. To solve the problem of low delivery rates, the paper integrates social attributes and space-time constrained characteristics and proposes social closeness and spatio-temporal interaction degree based on historical information. Then we design a routing algorithm (SC-STID) based on these two metrics. SC-STID takes into account the social closeness and spatio-temporal interaction of nodes to select relay nodes comprehensively and uses the delivered message deletion mechanism to remove the copies of delivered messages from the network. Experimental data shows that SC-STID improves the success rate of message delivery and reduces network resource consumption compared to existing routing algorithms (Prophet, Epidemic, ORRF) in a sparse opportunistic network of nodes with social attributes.

关键词： Measurement Epidemics Conferences Software algorithms Routing Software Relays

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Short-Time Traffic Flow Prediction Based on K-means++ and LSTM

Short-Time Traffic Flow Prediction Based on K-means++ and LS...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Jia Li Gang Xu Yuan Cheng Baoqi Huang Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot China College of Computer Science Inner Mongolia University Hohhot China

With the gradual increase in the number of vehicles owned by people, the problem of urban traffic congestion is becoming more and more serious. Timely and accurate traffic flow prediction helps travel vehicles to plan their routes reasonably, avoid congested roads and reduce environmental pollution. In order to take full advantage of the daily periodicity of the traffic flow of a road section, K-means++ with improved initial clustering centroids is applied to analyze the similarity of the daily traffic flow patterns and classify the traffic flow patterns with day as the time interval. One can separate the traffic patterns affected by different factors, and the other can exclude individual days containing abnormal data trends formed by unexpected road conditions, which can save the deep learning model from the interference of other irrelevant data features. Finally, a Long Short-Term Memory (LSTM) model is combined to predict the traffic flow value at the next time point based on the data of the previous hour. In the experiment, the K-means-LSTM model can significantly reduce the prediction error of short-time traffic flow and has better prediction performance by comparing with the prediction results of the support vector machine (SVR) model optimized by genetic algorithm (GA) and the LSTM model.

关键词： Adaptation models Roads Time series analysis Predictive models Traffic control Market research Data models

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Online Public Transit Ridership Flow Estimation through Passive WiFi Sensing

Online Public Transit Ridership Flow Estimation through Pass...

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第34届中国控制与决策会议

作者： Wenbo Chang Baoqi Huang Bing Jia Wuyungerile Li Gang Xu Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia A.R.Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University

Online public transit ridership flow information is helpful to improve the service quality of urban public transportation and travel experience of *** WiFi sensing collects WiFi probe requests sent by mobile devices in a non-intrusive manner,and can thus be used to monitor ridership *** with the existing methods based on manual configurations or video surveillance,passive WiFi sensing based methods demonstrate the advantages of small interference,large coverage,low cost and simple *** recent years,researchers proposed some offline methods based on passive WiFi sensing,but offering online information is still *** paper develops a public transit ridership flow monitoring system using customized WiFi sniffers,and proposes an algorithm to implement online public transit ridership flow ***,the proposed algorithm uses the bidirectional long short-term memory neural network(BiLSTM) to capture the bidirectional time series features contained in WiFi sensing data,and further integrates the attention mechanism to focus on the critical features in relation to *** experimental results,carried out on four bus routes in Hohhot,China,show that the RMSE and MAE errors of the proposed algorithm are 3.96 and 3.1,respectively,which are better than those of the other 7 commonly used online ***,the accuracy of the proposed algorithm is 5% lower than that of the corresponding offline algorithm,but it is evident that our online algorithm will stimulate more practical applications.

关键词： Passive WiFi sensing Online Public transit ridership flow Attention mechanism BiLSTM

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A Wildlife Tracking Scheme in Forest Area Based on UAV and Sensors

A Wildlife Tracking Scheme in Forest Area Based on UAV and S...

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作者： Hongze Qiao Xiangyu Bai Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer ScienceInner Mongolia University

In this paper, we aim to propose a wildlife tracking scheme for forest areas similar to the greater Khingan mountains in China. The classic outdoor positioning technology, such as global navigation satellite system(GNSS), has the advantages of all-weather positioning, global coverage and high positioning accuracy. However, due to more signal occlusion in forest areas, the GNSS positioning effect is not good. Another example is inertial navigation system(INS),which does not rely on external information or limited positioning environment, and has high positioning accuracy. However, as the positioning time increases, the positioning error of INS gradually accumulates, resulting in poor positioning effect. In this paper, we propose an animal tracking scheme based on Time difference of arrival(TDOA) localization algorithm and extended Kalman filter(EKF) with the help of Unmanned aerial vehicle(UAV) and wireless sensor network(WSN) to solve the problem of wildlife monitoring in forest areas. The simulation results show that our scheme is feasible.

关键词： Time difference of arrival location Target tracking Extended Kalman filter Sensor networks

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