2022 18th International Conference on Mobility, Sensing and Networking (MSN)最新文献

Recognition of Abnormal Proxy Voice Traffic in 5G Environment Based on Deep Learning* 基于深度学习的5G环境下异常代理语音流量识别*

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00070

Hongce Zhao, Shunliang Zhang, Xianjin Huang, Zhuang Qiao, Xiaohui Zhang, Guanglei Wu

{"title":"Recognition of Abnormal Proxy Voice Traffic in 5G Environment Based on Deep Learning*","authors":"Hongce Zhao, Shunliang Zhang, Xianjin Huang, Zhuang Qiao, Xiaohui Zhang, Guanglei Wu","doi":"10.1109/MSN57253.2022.00070","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00070","url":null,"abstract":"With the commercial use of the fifth generation (5G), the rapid popularization of mobile Over- The- Top (OTT) voice applications has brought high-quality voice communication methods to users. The intelligent Internet in the 5G era makes communication terminals not limited to mobile phones. The complex communication environment has higher requirements for the security of data transmission between various terminals to prevent the system from being monitored or breached. At present, many OTT users use encrypted proxy technology to get rid of certain restrictions of network operators, prevent their private information from leaking, and ensure communication security. However, in some cases the encryption proxy may be subject to configuration error or maliciously attacked makes the encryption ineffective. The resulting abnormal proxy traffic may cause privacy leakage when users use voice services. However, little effort has been put on fingerprint the effectiveness of encryption for proxy voice traffic in a 5G environment. To this end, we adopt the VGG deep learning method to identify agent speech traffic, compare it with common deep learning methods, and study the impact on model performance with less abnormal traffic. Extensive experimental results show that the deep learning method we use can identify abnormal encrypted proxy voice traffic with the accuracy up to 99.77%. Moreover, VGG outperform other DL methods on indentifying the encryption algorithms of normal encrypted proxy traffic.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115622501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer Learning based City Similarity Measurement Methods 基于迁移学习的城市相似性度量方法

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00107

Chenxin Qu, Xiaoping Che, Ganghua Zhang

引用次数: 0

RTSS: Robust Tuple Space Search for Packet Classification RTSS:数据包分类的鲁棒元组空间搜索

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00157

Jiayao Wang, Ziling Wei, Baosheng Wang, Shuhui Chen, Jincheng Zhong

引用次数: 0

An Efficient and Secure Node-sampling Consensus Mechanism for Blockchain Systems 区块链系统中高效安全的节点采样共识机制

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00067

Zhelin Liang, Hao Xu, Xiulong Liu, Shan Jiang, Keqiu Li

{"title":"An Efficient and Secure Node-sampling Consensus Mechanism for Blockchain Systems","authors":"Zhelin Liang, Hao Xu, Xiulong Liu, Shan Jiang, Keqiu Li","doi":"10.1109/MSN57253.2022.00067","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00067","url":null,"abstract":"The consensus mechanism plays a pivotal role in guaranteeing the security and consistency of blockchain systems and substantially affects system performance. However, an increasing number of blockchain nodes degrade the consensus performance dramatically because of the high communication complexity in traditional consensus mechanisms. In this paper, we propose NS-consensus, a secure node-sampling blockchain consensus mechanism reducing the communication complexity significantly. The key novelty lies in the sampling of blockchain nodes so that the leader only needs to interact with the sampling nodes in each consensus epoch. However, NS-consensus imposes two challenges in determining an optimal sample size and denying malicious proposals. To address the challenges, we determine the sample size under the constraints of a confidence level and a margin of error to enhance communication efficiency without compromising system security. Furthermore, we design a mechanism to enable the leader to interact with all blockchain nodes in the last consensus phase, ensuring the denial of malicious proposals. The extensive experimental results indicate that NS-consensus outperforms the state-of-the-art with up to 175.1% higher system throughput and 79.9% lower time overhead in the sampling phases.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122714587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Keynotes: MSN 2022 主题演讲:MSN 2022

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/msn57253.2022.00010

引用次数: 0

Leakage Detection via Edge Processing in LoRaWAN-based Smart Water Distribution Networks 基于lorawan的智能配水网络边缘处理的泄漏检测

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00047

D. Garlisi, Gabriele Restuccia, I. Tinnirello, F. Cuomo, I. Chatzigiannakis

{"title":"Leakage Detection via Edge Processing in LoRaWAN-based Smart Water Distribution Networks","authors":"D. Garlisi, Gabriele Restuccia, I. Tinnirello, F. Cuomo, I. Chatzigiannakis","doi":"10.1109/MSN57253.2022.00047","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00047","url":null,"abstract":"The optimization and digitalization of Water Distribution Networks (WDNs) are becoming key objectives in our modern society. Indeed, WDNs are typically old, worn and obsolete. These inadequate conditions of the infrastructures lead to significant water loss due to leakages inside pipes, junctions and nodes. It has been measured that in Europe the average value of lost water is about 26 %. Leakage control in current WDNs is typically passive, repairing leaks only when they are visible. Emerging Low Power Wide Area Network (LPWAN) technologies, and especially IoT ones, can help monitor water consumption and automatically detect leakages. In this context, LoRaWAN can be the right way to deploy a smart monitoring system for WDNs. Moreover, most of the current smart WDNs solutions just collect measurements from the smart metres and send the data to the cloud servers, in order to execute the intended analyses, in centralised way. In this paper, we propose new solutions to improve monitoring, leak management and prediction by exploiting edge processing capabilities inside LoRaWAN networks. Our approach is based on an IoT system of water sensors that are placed at junctions of the WDN to have measurements in correspondence to various smart metres in the network and Machine Learning (ML) algorithms to process the data directly at the edge in order to visualise and predict leakages. We present a numerical simulation tool useful to evaluate the suggested monitoring method. Based on our results, we examine whether it is possible to identify network leaks using the edges without having a complete or accurate overview of the collected measurements of the full WDN. System performance is shown separately at gateways network.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128506567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Rendezvous Delay-Aware Multi-Hop Routing Protocol for Cognitive Radio Networks 认知无线网络的交会延迟感知多跳路由协议

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00020

Zengqi Zhang, Sheng Sun, Min Liu, Zhongcheng Li, Qiuping Zhang

引用次数: 0

T2C: A Multi-User System for Deploying DNNs in a Thing-to-Cloud Continuum T2C:用于在物到云连续体中部署dnn的多用户系统

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00052

C. Hsieh, Praveen Venkateswaran, N. Venkatasubramanian, Cheng-Hsin Hsu

{"title":"T2C: A Multi-User System for Deploying DNNs in a Thing-to-Cloud Continuum","authors":"C. Hsieh, Praveen Venkateswaran, N. Venkatasubramanian, Cheng-Hsin Hsu","doi":"10.1109/MSN57253.2022.00052","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00052","url":null,"abstract":"The importance of IoT analytics in smart deploy-ments has resulted in an increased use of powerful Deep Neural Network (DNN) models to extract insights from the growing amount of IoT sensor data. Traditional approaches that entirely offload computation and model deployment to cloud servers have been shown to be inefficient due to network congestion and latency concerns. However, with the improved capabilities of IoT devices, it has now become possible to distribute and host DNNs across IoT devices, edge servers and the cloud. In this paper, we propose a multi-user system, called T2C, to dynamically choose, deploy, monitor and control DNN-driven IoT analytics in a thing-to-cloud continuum. T2C leverages strategies such as multi-task learning, hitchhiking, early exit, and dynamic reconfiguration, to maximize the number of served user requests while simultaneously satisfying accuracy and latency requirements. We propose a suite of deployment planning and reconfiguration algorithms to dynamically deploy and migrate DNN layers between IoT devices, edge servers, and the cloud. We implement T2C in a prototype testbed and show that our system: (i) achieves 6.8X throughput boost compared to baseline algorithms in the planning phase, and (ii) improves the satisfied ratio by up to 35% in the operation and reconfiguration phase.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127751722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Motion Propagation Prediction based Sim2Real Strategy Migration for Clutter Removal 基于运动传播预测的Sim2Real策略移动杂波去除

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00092

Jiaxin Zhang, Ping Zhang

{"title":"A Motion Propagation Prediction based Sim2Real Strategy Migration for Clutter Removal","authors":"Jiaxin Zhang, Ping Zhang","doi":"10.1109/MSN57253.2022.00092","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00092","url":null,"abstract":"When objects are densely placed, training in the simulation with artificial samples and removing clutter are helpful to reduce the cost and risk. However, the performance of control strategy decreases in sim2real is still a challenge. This paper introduces a clutter removal method of sim2real using object motion propagation prediction. In this method, based on deep reinforcement learning, push and grasp actions are used to remove clutter. The reward of push action is calculated based on the object divergence of quadtree. The action strategy is trained in the simulation environment. Due to the position error caused by the robot pushing the object in the simulation and real environment, the object motion propagation prediction network based on graph neural network is used to predict the pushing results in the real environment and replace the real push action to training pushing strategy to improve the reward value. The pushing strategy learned in the simulation is subject to fine-tuning based on differential evolution. Compared with applying the action strategy directly to the real environment, the method in this paper has higher action efficiency and completion rate.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of DoH Tunnels with Dual-Tier Classifier 基于双层分类器的DoH隧道检测

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00073

Yuqi Qiu, Baiyang Li, Liang Jiao, Yujia Zhu, Qingyun Liu

引用次数: 0