2021 17th International Conference on Mobility, Sensing and Networking (MSN)最新文献

Analyzing a 5G Dataset and Modeling Metrics of Interest 分析5G数据集和建模感兴趣的指标

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00027

Fidan Mehmeti, T. L. Porta

引用次数: 9

A Location-Aware Cross-Layer MAC Protocol for Vehicular Visible Light Communications 车辆可见光通信的位置感知跨层MAC协议

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00084

Agon Memedi, F. Dressler

{"title":"A Location-Aware Cross-Layer MAC Protocol for Vehicular Visible Light Communications","authors":"Agon Memedi, F. Dressler","doi":"10.1109/MSN53354.2021.00084","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00084","url":null,"abstract":"Vehicular Visible Light Communications (V-VLC) has emerged as a technology complementing RF-based Vehicle-toVehicle (V2V) communication. Indeed, such RF-based protocols have certain disadvantages due to the limited radio resources and the, in general, omnidirectional interference characteristics. Making use of LED head- and taillights, V-VLC can readily be used in vehicular scenarios. One of the challenging problems in this field is medium access; most approaches fall back to ALOHA or CSMA-based concepts. Thanks to modern matrix lights, VVLC can now also make use of Space Division Multiple Access (SDMA) features. In this paper, we present a novel approach for medium access in V-VLC systems. We follow a location-aware cross-layer concept, in which dedicated light sectors of matrix lights are used to avoid interference and thus collisions. We assess the performance of our protocol in an extensive simulation study using both a simple static scenario as well as a realistic urban downtown configuration. Our results clearly indicate the advantages of our location-aware protocol that exploits the space-division features of the matrix lights.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125533853","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: 4

Deep Learning for Cyber Deception in Wireless Networks 无线网络中网络欺骗的深度学习

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00086

Felix O. Olowononi, Ahmed H. Anwar, D. Rawat, Jaime C. Acosta, C. Kamhoua

{"title":"Deep Learning for Cyber Deception in Wireless Networks","authors":"Felix O. Olowononi, Ahmed H. Anwar, D. Rawat, Jaime C. Acosta, C. Kamhoua","doi":"10.1109/MSN53354.2021.00086","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00086","url":null,"abstract":"Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128267368","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: 3

The Algorithm of Multi-source to Multi-sink Traffic scheduling 多源到多汇聚流量调度算法

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00097

Yang Liu, Lei Liu, Zhongmin Yan, Jian-Qiang Hu

引用次数: 2

One-Class Support Vector Machine with Particle Swarm Optimization for Geo-Acoustic Anomaly Detection 基于粒子群优化的一类支持向量机地声异常检测

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00066

Dan Zhang, Yiwen Liang, Zhihong Sun, M. Mukherjee

引用次数: 0

Third International Workshop on Edge Computing and Artificial Intelligence based Sensor-Cloud System (ECAISS 2021) 第三届基于边缘计算和人工智能的传感器云系统国际研讨会(ECAISS 2021)

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/msn53354.2021.00013

引用次数: 0

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2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/msn53354.2021.00001

引用次数: 0

A Clustering Method of Encrypted Video Traffic Based on Levenshtein Distance 基于Levenshtein距离的加密视频流量聚类方法

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00017

L. Yang, Shaojing Fu, Yuchuan Luo, Yongjun Wang, Wentao Zhao

{"title":"A Clustering Method of Encrypted Video Traffic Based on Levenshtein Distance","authors":"L. Yang, Shaojing Fu, Yuchuan Luo, Yongjun Wang, Wentao Zhao","doi":"10.1109/MSN53354.2021.00017","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00017","url":null,"abstract":"In order to detect the playback of illegal videos, it is necessary for supervisors to monitor the network by analyzing traffic from devices. However, many popular video sites, such as YouTube, have applied encryption to protect users’ privacy, which makes it difficult to analyze network traffic at the same time. Many researches suggest that DASH (Dynamic Adaptive Streaming over HTTP) will leak the information of video segmentation, which is related to the video content. Consequently, it is possible to analyze the content of encrypted video traffic without decryption. At present, most of the encrypted video traffic analysis adopts supervised learning methods, and there is little research on its unsupervised methods. Analysts are usually faced with unlabeled data, in reality, so the existing approaches will not work. The encrypted video traffic analysis methods based on unsupervised learning are required. In this paper, we proposed a clustering method based on Levenshtein distance for title analysis of encrypted video traffic. We also run a thorough set of experiments that verify the robustness and practicability of the method. As far as I am concerned, it is the first work to apply cluster analysis for encrypted video traffic analysis.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"41 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133049719","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: 2

FIDS: A Federated Intrusion Detection System for 5G Smart Metering Network FIDS: 5G智能计量网络的联邦入侵检测系统

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00044

Parya Haji Mirzaee, M. Shojafar, Zahra Pooranian, Pedram Asef, H. Cruickshank, R. Tafazolli

{"title":"FIDS: A Federated Intrusion Detection System for 5G Smart Metering Network","authors":"Parya Haji Mirzaee, M. Shojafar, Zahra Pooranian, Pedram Asef, H. Cruickshank, R. Tafazolli","doi":"10.1109/MSN53354.2021.00044","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00044","url":null,"abstract":"In a critical infrastructure such as Smart Grid (SG), providing security of the system and privacy of consumers are significant challenges to be considered. The SG developers adopt Machine Learning (ML) algorithms within the Intrusion Detection System (IDS) to monitor traffic data and network performance. This visibility safeguards the SG from possible intrusions or attacks that may trigger the system. However, it requires access to residents’ consumption information which is a severe threat to their privacy. In this paper, we present a novel method to detect abnormalities on a large scale SG while preserving the privacy of users. We design a Federated IDS (FIDS) architecture using Federated Learning (FL) in a 5G environment for the SG metering network. In this way, we design Federated Deep Neural Network (FDNN) model that protects customers’ information and provides supervisory management for the whole energy distribution network. Simulation results for a real-time dataset demonstrate the reasonable improvement of the proposed FDNN model compared with the state-of-the-art algorithms. The FDNN achieves approximately 99.5% accuracy, 99.5% precision/recall, and 99.5% f1-score when comparing with classification algorithms.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585340","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: 9

Joint Task Offloading and Resource Allocation for MEC Networks Considering UAV Trajectory 考虑无人机轨迹的MEC网络联合任务卸载与资源分配

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00054

Xiyu Chen, Yangzhe Liao, Qingsong Ai, Ke Zhang

{"title":"Joint Task Offloading and Resource Allocation for MEC Networks Considering UAV Trajectory","authors":"Xiyu Chen, Yangzhe Liao, Qingsong Ai, Ke Zhang","doi":"10.1109/MSN53354.2021.00054","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00054","url":null,"abstract":"Owing to the high flexibility and mobility, unmanned aerial vehicles (UAVs) have attracted significant attention from both academia and industry communities, especially in the UAVempowered mobile edge computing (MEC) networks. However, the repetitiveness of tasks generated by user equipments (UEs) has not been fully analyzed. In this paper, a UAV-empowered MEC network architecture is proposed. Computation tasks are divided into two categories, i.e., private tasks and public tasks, which can be executed locally or offloaded to UAVs utilized as flying MEC servers for task execution. The aim of this paper is to optimize task execution latency and network energy consumption by jointly considering UEs’ offloading decisions and UAVs’ route planning. To solve the challenging formulated optimization problem, an enhanced block coordinate descent algorithm is proposed, which is utilized in conjunction with the differential evolution and penalty function method. The simulation results demonstrate that the proposed scheme outperforms the random offloading strategy and fixed route strategy regarding the cumulative cost and time cost.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114937186","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: 3