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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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On the Fine-Grained Crowd Analysis via Passive WiFi Sensing

TechRxiv

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TechRxiv 2023年

作者： Hao, Lifei Huang, Baoqi Jia, Bing Mao, Guoqiang The Engineering Research Center of Ecological Big Data Ministry of Education The Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing The College of Computer Science Inner Mongolia University Hohhot010021 China The Research Institute of Smart Transportation Xidian University Xi’an710071 China

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps in a way similar to image reconstruction;to combat the anonymity caused by MAC randomization, following the identification of local high-density crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a bidirectional convolutional LSTM based model is employed to infer LHDC speeds;to overcome the absence of passive WiFi sensing datasets for model training, three semi-synthetic datasets are produced by emulating passive WiFi sensing with practical pedestrian tracking datasets. Extensive experiments confirm that, the proposed scheme significantly outperforms existing WiFi-based methods in terms of crowd density estimation and provides superior crowd speed estimation. More importantly, the scheme can also produce consistent crowd states on a real-world dataset, revealing that it has the ability to support accurate, visualized and real-time crowd monitoring in large surveillance areas. © 2023, CC BY-NC-SA.

关键词： Convolution

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Trajectory Privacy Protection Method based on Shadow vehicles

Trajectory Privacy Protection Method based on Shadow vehicle...

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

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

Location Based Services (LBS) are widely used in VANETs to provide a variety of services for vehicles. The trajectory privacy protection has attracted wide attention. However, there are some shortcomings in the schemes proposed at home and abroad. Therefore, this paper proposes a protection scheme based on shadow vehicles. First, the Genetic Algorithm is used to predict the trajectory, and suitable shadow vehicles are selected based on the deviation of the predicted trajectory. Then, a number of fake queries are inserted into the queries of real vehicles and shadow vehicles, finally a query matrix is formed and sent it to the LBS server for query. Fake queries make vehicle’s paths intersect, which can prevent the reverse mapping from vehicle’s identity to query content, so that the attacker can’t distinguish the complete trajectory of the target vehicle and the scheme can prevent continuous query attacks. Experiments have proved that the scheme improves the prediction similarity, the selected shadow vehicles are similar to the predicted trajectory of the real vehicle and the privacy protection of the trajectory is enhanced.

关键词： Location awareness Privacy Trajectory Servers Genetic algorithms

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A Channel Adaptive WiFi Indoor Localization Method based on Deep Learning

A Channel Adaptive WiFi Indoor Localization Method based on ...

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IEEE Conference on Wireless Communications and networking

作者： Lifei Hao Baoqi Huang Hao Hong Bing Jia Wuyungerile Li Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9781728195056;9781728195063

With the increasing demand on Indoor Location-Based Services (ILBS), various positioning technologies had emerged in the past decades, and WiFi-based approach is one of the most promising ones. However, the existing WiFi localization methods fail to take into account the disparate influence of packets transmitted in different channels so as to inhibit the further improvement of localization accuracy. Therefore, we present CADNN: a Channel Adaptive WiFi localization method based on Deep Neural Network (DNN). Specifically, a comprehensive analysis on signal attenuations in different channels along with error analysis based on Cramer-Rao Lower Bound (CRLB) is conducted in theory. Then, the channel set splitting scheme for practical localization to leverage multi-channel features is proposed. Finally, we design a localization framework using multi-objective regression DNN to adapt Received Signal Strength (RSS) measurements from different channel sets. The results from real-world experiments confirm the effectiveness of channel adaptive and show that CADNN can improve localization accuracy by at least 25.3% and 19.5% respectively on the two datasets and it can serve thousands users within one second.

关键词： Location awareness Deep learning Adaptive systems Error analysis Conferences Neural networks Measurement uncertainty

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Research on VANET Network Coding Strategy in Expressway Scene

Research on VANET Network Coding Strategy in Expressway Scen...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Hua Zhang Xiangyu Bai Lei Yao Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing School of Computer Science Inner Mongolia University Hohhot Inner Mongolia Autonomous Region China

ISBN: (纸本)9781665432863

VANET is a real-time, intelligent transportation network featuring motion patterns limited by roads, diverse transmission data, and frequent topological changes. In the highway scene, the network coding and the roadside unit are introduced to assist the data transmission in order to solve such problems as single data transmission path and short communication link duration. When the vehicle node transmits data, it will first determine the type of vehicle node at this time, and select the best transmission path as the main path of the encoded data packet according to the relative position information of the vehicle nodes, the difference of the requested content storage nodes, and the vehicle density. The path acts as a secondary path for the transmission of encoded packets. Finally, the routing strategy proposed in this paper is implemented by the opportunistic network simulator ONE simulation. According to the experimental results, the routing strategy proposed in this paper can effectively improve the message delivery rate, reduce the average network delay and network overhead.

关键词： Roads Vehicular ad hoc networks Transportation Disaster management Network coding Routing Real-time systems

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Key nodes recognition in opportunistic network 6th

Key nodes recognition in opportunistic network

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Wang, Zhifei Xu, Gang Wei, Fengqi Qi, Zhihan He, Liqiang College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot China Geomechanica Inc. Toronto Canada

ISBN: (纸本)9789811590306

There are always communication fragmented regions in opportunistic networks, and Ferry nodes which can periodicity commute between different fragmented regions always be placed in opportunistic networks. At present, the research on Ferry nodes in opportunistic networks mainly focus on the cache management, energy balance and routing algorithm optimization, meanwhile, researches on identifying Ferry nodes in a strange network are less. On the basis of the importance of structure holes and k-cores, this paper puts forward the index to evaluate the dynamic importance of nodes in opportunistic network, and proposes an importance evaluation algorithm of nodes in opportunistic networks based on it, which is used to identify the Ferry nodes clusters in strange networks. Conclusions can draw through experiments that the proposed model has good applicability and can identify Ferry nodes in networks accurately. © Springer Nature Singapore Pte Ltd 2020.

关键词： Clustering algorithms

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

Counting Mobile Devices With Physical Layer Measurements in ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

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

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.

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LoCoCa: Location-Context-Capacity Aware Cost Economizing in Edge-Cloud Systems

LoCoCa: Location-Context-Capacity Aware Cost Economizing in ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yuanze Li Chao Qiu Xiaofei Wang Cheng Zhang Wenyu Wang Shizhan Lan Jing Jiang College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Institute of Technology Tianjin University of Finance and Economics Tianjin China Shanghai Zhuichu Networking Technologies Co. Ltd. China China Mobile Guangxi institute Co Ltd Guangxi China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China

Nowadays, real-time interactive content services have been the most dazzling sector of next-generation Internet. The high-quality perceptions of virtual scenes have given rise to the strict requirements of high bandwidth and low latency, where the edge-cloud system promises several benefits. However, there still remain prominent challenges, when taking the economical efficiency into consideration, including location-heterogeneity, context-directability, and capacity-exploitation. In this paper, we propose a location-context-capacity aware bandwidth cost economizing strategy in the edge-cloud system, i.e., LoCoCa. LoCoCa adopts server pools partition mechanism, then achieving the optimal burstable billing in each pool. Here, a location-aware graph construction and partition algorithm is designed to solve the server pools partition problem. Then an improved burstable billing optimization mechanism, with a context index and an adaptive bandwidth capacity, is also proposed to economize bandwidth costs. Finally, the realistic edge-cloud company's trace-based experimental results verify LoCoCa reduces bandwidth costs by 81.83 %, compared with the baselines.

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Automation Test Tool for the Page Load Time of mobile Applications

Automation Test Tool for the Page Load Time of Mobile Applic...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Wentao Zeng Xiangyu Bai Kexin Zhou Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing School of Computer Science Inner Mongolia University Hohhot Inner Mongolia Autonomous Region China

ISBN: (纸本)9781665432863

When users use a mobile phone application for the first time, except for functional requirements the more direct experience is the performance of the application on the phone. The main work of this article is designing and implementing load time test methods on the Android application pages. Under the joint action of the Appium automated test framework and Maven package management tool, the test process of the tested application is designed such as the order of specific page jumps, the relationship between package structures and classes, etc. After writing the function of automation code and verifying that the page can be successfully jumped by non-human intervention, continue to modify the code to complete the special automated test through the toolkit developed in this article, and finally get the load time result set of pages.

关键词： Codes Automation Disaster management Writing mobile applications Information and communication technology Smart phones

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RNN-Based User Trajectory Prediction Using a Preprocessed Dataset

RNN-Based User Trajectory Prediction Using a Preprocessed Da...

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IEEE International Conference on Wireless and mobile computing, networking And Communications (WiMob)

作者： Nasrin Bahra Samuel Pierre Mobile Computing and Networking Research Laboratory (LARIM) Polytechnique de Montréal Montreal Canada

ISBN: (数字)9781728197227

ISBN: (纸本)9781728197234

Future mobile networks are rightly expected to face the prospect of limited available resources. Continuous technological advances and growing number of mobile devices highlight the importance of further improving the performance of mobile networks. User mobility poses technical problems in network management. It is essential to ensure a satisfactory level of quality of service for users. To achieve this goal, self organizing networks (SONs) are potential solutions to fulfill the requirements of users using learning algorithms. In this paper, we propose an intelligent mobility model to predict future trajectory of the mobile user in mobile networks. The proposed approach has two main parts, including mobility data preparation and user mobility prediction. Our primary focus is on providing a carefully tailored mobility data from raw mobility datasets using line simplification techniques. Next, we use the accurately prepared data for learning user mobility behaviour and predicting user future trajectory using recurrent neural networks and its variants. Simulation results show a substantial decrease in execution time from 4616s to 932s for the best case. The proposed learning approach obtains a loss value of 0.10 using a model based on long short term memory (LSTM).

关键词： Trajectory Data models Predictive models Hidden Markov models Neural networks Data transfer Global Positioning System

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