2021 17th International Conference on Mobility, Sensing and Networking (MSN)最新文献_第10页

Mobility-Aware Efficient Task Offloading with Dependency Guarantee in Mobile Edge Computing Networks 移动边缘计算网络中具有依赖保障的移动感知高效任务卸载

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00061

Qi Wu, Guolin Chen, Xiaoxia Huang

引用次数: 0

Soft Actor-Critic Algorithm for 360-Degree Video Streaming with Long-Term Viewport Prediction 具有长期视口预测的360度视频流软Actor-Critic算法

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00075

Xiaosong Gao, Jiaxin Zeng, Xiaobo Zhou, Tie Qiu, Keqiu Li

{"title":"Soft Actor-Critic Algorithm for 360-Degree Video Streaming with Long-Term Viewport Prediction","authors":"Xiaosong Gao, Jiaxin Zeng, Xiaobo Zhou, Tie Qiu, Keqiu Li","doi":"10.1109/MSN53354.2021.00075","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00075","url":null,"abstract":"In the tile-based 360-degree video streaming, it is essential to predict future viewport and to allocate higher bitrates to tiles inside the predicted viewport to optimize the Quality of Experience (QoE) of the users. However, the majority of existing work focuses on short-term viewport prediction, which is prone to rebuffering in dynamic network conditions. On the other hand, the recently developed on-policy Deep Reinforcement Learning (DRL)-based bitrate allocation approaches suffer from poor sample efficiency. To address these issues, in this paper we present a tile-based adaptive 360-degree video streaming system, named LS360, which consists of long-term viewport prediction and adaptive bitrate allocation. First, we propose a Long Short-Term Memory (LSTM)-based viewport prediction model to make use of the heatmap feature from all users’ previous movement information and the target user’s fixation movement feature to improve prediction accuracy. Next, we employ the off-policy Soft Actor-Critic (SAC) algorithm to make optimal tile bitrate allocation decisions by taking the predicted long-term viewport, playback buffer, and bandwidth-related information into account. Experiments on real-world datasets demonstrate that LS360 outperforms state-of-the-art streaming algorithms in terms of long-term viewport prediction accuracy and QoE under different bandwidth conditions.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"8 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":"128573979","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

Network Security by Merging two Robust Tools from the Mathematical Firmament 通过合并两个来自数学领域的鲁棒工具实现网络安全

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00094

Andreas Andreou, C. Mavromoustakis, G. Mastorakis, J. M. Batalla

{"title":"Network Security by Merging two Robust Tools from the Mathematical Firmament","authors":"Andreas Andreou, C. Mavromoustakis, G. Mastorakis, J. M. Batalla","doi":"10.1109/MSN53354.2021.00094","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00094","url":null,"abstract":"Significant advances in wireless detection, networking, and IoT technologies presuppose network security and confidentiality demand. Therefore, we develop a novel text encryption framework that has provable security against attacks on cryptosystems. The framework is based on fundamental mathematics and specifically on the Pell-Lucas sequence in conjunction with elliptic curves. We elaborate the plain text by the implementation of three basic steps. Initially, by applying a cyclic shift on the symbol set, we obtain a meaningless plain text. After that, we conceal the elements of the scattered plain text from the adversaries by using the Pell-Lucas sequence, a weight function, and a binary sequence. The binary sequence encodes each component of the diffused plain text into real numbers. In the final step, the encoded scattered plain text is confused by creating permutations over elliptic curves. We then prove that the proposed encryption framework has provable security against brute-force and known-plaintext attack. It is also extremely secure compared with fundamental spacing analysis.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"1 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":"124407767","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

Flow Transformer: A Novel Anonymity Network Traffic Classifier with Attention Mechanism 流量转换器:一种具有注意机制的新型匿名网络流量分类器

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00045

Ruijie Zhao, Yiteng Huang, Xianwen Deng, Zhi Xue, Jiabin Li, Zijing Huang, Yijun Wang

{"title":"Flow Transformer: A Novel Anonymity Network Traffic Classifier with Attention Mechanism","authors":"Ruijie Zhao, Yiteng Huang, Xianwen Deng, Zhi Xue, Jiabin Li, Zijing Huang, Yijun Wang","doi":"10.1109/MSN53354.2021.00045","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00045","url":null,"abstract":"Supervising anonymity network is a critical issue in the field of network security, and traditional traffic analysis methods cannot cope with complex anonymity traffic. In recent years, the traffic analysis method based on deep learning has achieved good performance. However, most of the existing studies do not consider the temporal-spatial correlation of the traffic, and only use a single flow for classification. A few works take continuous flows as flow sequence for traffic classification, but they do not distinguish the different importance of each flow. To tackle this issue, we propose a novel flow-based traffic classifier called FLOW TRANSFORMER to classify anonymity network traffic. FLOW TRANSFORMER uses multi-head attention mechanism to set higher weights for important flows, and extracts flow sequence features according to the importance weights. Besides, the RF-based feature selection method is designed to select the optimal feature combination, which can effectively avoid the insignificant features from reducing the performance and efficiency of the classifier. Experimental results on two real-world traffic datasets demonstrate that the proposed method outperforms state-of-the-art methods with a large margin.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"77 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":"125637153","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

DVC: An Deductable Video Coding for Video Broadcast in Highly Dynamic Networks 高动态网络中视频广播的可演绎视频编码

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00090

Yunhuai Liu, Yonggui Huang, Sheng Gu

{"title":"DVC: An Deductable Video Coding for Video Broadcast in Highly Dynamic Networks","authors":"Yunhuai Liu, Yonggui Huang, Sheng Gu","doi":"10.1109/MSN53354.2021.00090","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00090","url":null,"abstract":"Today’s mobile video codecs suffer from vulnerability to the high network dynamics. When the network effective bandwidth varies in a large range and is not predictable, e.g., in a high-speed mobile environment or being with intense wireless interference, the videos are continuously choppy and jerky, waiting for the buffer to fill before playing. In this paper, we design and implement a novel video codec scheme called DVC that provides the following properties: 1) Keep the video compression rate as the modern video codec schemes, such as H.265; 2) Non-blocking playback for video decoding; 3) High tolerance to network dynamics. Different from existing robust video codec designs that try to provide certain redundancy to compensate the network transmission losses, DVC assumes that transmission losses are inevitable. And thus the design philosophy is to mitigate the consequence of the data loss by restraining the error propagation caused by the losses, rather than the loss itself. We implement DVC with a encoder and a decoder and conducted comprehensive evaluations based on the prototype systems. Experimental results show that the video quality (PSNR) can be significantly improved by 4dB to 10dB, espeically when the videos have low amount of movement, e.g., news report.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"18 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":"134300637","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

Long-Term Visual Localization with Semantic Enhanced Global Retrieval 基于语义增强全局检索的长期视觉定位

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00057

Hongrui Chen, Yuan Xiong, Jingru Wang, Zhong Zhou

{"title":"Long-Term Visual Localization with Semantic Enhanced Global Retrieval","authors":"Hongrui Chen, Yuan Xiong, Jingru Wang, Zhong Zhou","doi":"10.1109/MSN53354.2021.00057","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00057","url":null,"abstract":"Visual localization under varying conditions such as changes in illumination, season and weather is a fundamental task for applications such as autonomous navigation. In this paper, we present a novel method of using semantic information for global image retrieval. By exploiting the distribution of different classes in a semantic scene, the discriminative features of the scene’s structure layout is embedded into a normalized vector that can be used for retrieval, i.e. semantic retrieval. Color image retrieval is based on low-level visual features extracted by algorithms or Convolutional Neural Networks (CNNs), while semantic retrieval is based on high-level semantic features which are robust in scene appearance variations. By combining semantic retrieval with color image retrieval in the global retrieval step, we show that these two methods can complement with each other and significantly improve the localization performance. Experiments on the challenging CMU Seasons dataset show that our method is robust across large variations of appearance and achieves state-of-the-art localization performance.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"238 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":"116282909","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

The Design and Implementation of an Efficient Quaternary Network Flow Watermark Technology 一种高效的四元网络流量水印技术的设计与实现

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00117

Lusha Mo, Gaofeng Lv, Baosheng Wang, Guanjie Qiao, Jing Tan

{"title":"The Design and Implementation of an Efficient Quaternary Network Flow Watermark Technology","authors":"Lusha Mo, Gaofeng Lv, Baosheng Wang, Guanjie Qiao, Jing Tan","doi":"10.1109/MSN53354.2021.00117","DOIUrl":"https://doi.org/10.1109/MSN53354.2021.00117","url":null,"abstract":"With the increasing demand for network security, because passive traffic analysis technology has the characteristics of low efficiency, high overhead, and susceptibility to interferences, network flow watermark technology has emerged. As an active traffic analysis method, network flow watermark technology can effectively track malicious anonymous communication users and real attackers behind the stepping-stone chain, which has the advantages of high accuracy and low overhead. However, the existing flow watermark codec is embedded in the software, which is only suitable for low-rate or small-sample network traffic. In the face of modern network high-rate data stream (such as 100gbps per port), it has exceeded the software and traditional switch processing power. In order to improve the coding efficiency and processing capacity of network flow watermark, this paper combines flow watermark technology with smart NIC (Network Interface Card), and proposes an efficient quaternary network flow watermark technology, deployed in the switch to form an efficient dynamic watermark mechanism. Theoretical analysis and experimental results show that the network flow watermark technology can efficiently process high-rate data stream, with higher coding efficiency and good robustness to disturbances.","PeriodicalId":215772,"journal":{"name":"2021 17th International Conference on Mobility, Sensing and Networking (MSN)","volume":"11 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":"121772533","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

From Wired to Wireless BMS in Electric Vehicles 从有线到无线的电动汽车管理系统

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00049

F. A. R. Vija, Samuel Cregut, Georgios Z. Papadopoulos, N. Montavont

引用次数: 2

A Node Importance Ranking Method Based on the Rate of Network Entropy Changes 基于网络熵变化率的节点重要性排序方法

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/MSN53354.2021.00021

Qian Chen, Xian Zhao, Wenyue Sun, Changda Wang

引用次数: 0

Third International Workshop on Artificial Intelligence Applications in Internet of Things (AI2OT 2021) 第三届物联网人工智能应用国际研讨会(aiot 2021)

2021 17th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2021-12-01 DOI: 10.1109/msn53354.2021.00012

引用次数: 0