IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)最新文献_第5页

Sharing the Surface: RIS-aided Distributed Mechanism Design for Hybrid Beamforming in Multi-cell Multi-user Networks 共享表面:多小区多用户网络中混合波束形成的ris辅助分布式机制设计

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484464

Boya Di

引用次数: 2

Toward Automatically Generating Privacy Policy for Smart Home Apps 智能家居应用程序自动生成隐私策略

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484530

Youqun Li, Yichi Zhang, Haojin Zhu, Suguo Du

{"title":"Toward Automatically Generating Privacy Policy for Smart Home Apps","authors":"Youqun Li, Yichi Zhang, Haojin Zhu, Suguo Du","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484530","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484530","url":null,"abstract":"Modern Smart Home platforms offer various applications, which should follow the platform privacy policies so that end users and regulators are informed of Sensitive Personal Information (SPI) related operations. However, the generalized privacy policies by Smart Home platforms fail to explain specific SPI related operations for individual applications. Meanwhile, according to previous works, potential SPI leaks may occur due to insufficient surveillance. In this paper, we propose the first system to automatically generate fine-grained privacy policies for individual applications through static code analysis and natural language techniques. First, from the code we extract the control flow graph and the SPI data flows. Then, we use a Naive Bayes model to transfer the data flows into verb-object phrases. Finally, we populate a pre-prepared privacy policy template with the previously generated phrases. We evaluate our system on Samsung SmartThings platform. The experimental results show that: 1) Our system can accurately extract SPI related operations from Smart Home applications; 2) The privacy policies created by our system are fine-grained and easily understandable; 3) We demonstrate the efficacy of the proposed system on a real world data-set of almost 250 apps.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330664","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

DA-WDGN: Drone-Assisted Weed Detection using GLCM-M features and NDIRT indices DA-WDGN:基于GLCM-M特征和NDIRT指数的无人机辅助杂草检测

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484598

G. Raja, K. Dev, Nisha Deborah Philips, S. Suhaib, M. Deepakraj, R. Ramasamy

{"title":"DA-WDGN: Drone-Assisted Weed Detection using GLCM-M features and NDIRT indices","authors":"G. Raja, K. Dev, Nisha Deborah Philips, S. Suhaib, M. Deepakraj, R. Ramasamy","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484598","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484598","url":null,"abstract":"The exponential growth of drone technology and its computational methods has led to a surge in agricultural applications employing drones. In this paper, a Drone-Assisted Weed Detection using a Modified multichannel Gray Level Co-Occurrence Matrix (GLCM-M) and Normalised Difference Index with Red Threshold (NDIRT) indices (DA-WDGN) is proposed to aid in the process of weed detection. In DA-WDGN, the drones combine both information and communication technologies for the far-field data acquisition and precise detection of weeds. Accurate detection of weeds limits the need for pesticides and helps to protect the environment. Traditional systems use an object-oriented classification system for weed detection, which suffer from the issue of close similarities between the shape features of crop plants and weeds, making it impossible to uniquely distinguish the weeds. Therefore in the DA-WDGN system, shape, texture, and spectral features are integrated to establish a unique pattern for every plant. These patterns are then used to differentiate between crops and weeds. The proposed DA-WDGN system improves the accuracy of weed detection to 99.4% thereby establishing its supremacy over other conventional weed detection algorithms.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116593264","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: 5

A Network Intrusion Detection Method Based on CNN and CBAM 基于CNN和CBAM的网络入侵检测方法

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484553

Yang Liu, Jian Kang, Yiran Li, Bin Ji

引用次数: 6

1024-QAM Analog Waveform Transmission Over a Seamless Fiber-Wireless System in W Band 在W波段无缝光纤无线系统中传输1024-QAM模拟波形

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484620

P. Dat, A. Kanno, N. Yamamoto, T. Kawanishi

引用次数: 0

DeepSafe: A Hybrid Kitchen Safety Guarding System with Stove Fire Recognition Based on the Internet of Things DeepSafe:基于物联网的炉灶火灾识别混合厨房安全防护系统

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484547

Lien-Wu Chen, Hsing-Fu Tseng

{"title":"DeepSafe: A Hybrid Kitchen Safety Guarding System with Stove Fire Recognition Based on the Internet of Things","authors":"Lien-Wu Chen, Hsing-Fu Tseng","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484547","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484547","url":null,"abstract":"This paper designs and implements a deep learning based hybrid kitchen safety guarding system, called DeepSafe, using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). In the sensing mode, the DeepSafe system can prevent the kitchen from fire/explosion disasters by detecting gas concentration, recognizing fire intensity, and estimating vibration levels. In the control mode, the DeepSafe system can automatically block the gas source as detecting an abnormal event, remotely monitor the kitchen status via real-time streaming videos, and manually turn off the gas source using a smartphone as necessary. To accurately recognize the intensity of stove fire and detect abnormal fire intensity, deep learning based fire recognition methods using conventional and densely connected convolutional neural networks are developed to further improve the recognition accuracy of DeepSafe. In particular, the prototype consisting of an Android based APP and a Raspberry Pi based IoT device with the gas detector, image sensor, and 3-axis accelermeter are implemented to verify the feasibility and correctness of our DeepSafe system.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124956885","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

Resource Allocation for Low-Latency NOMA-V2X Networks Using Reinforcement Learning 基于强化学习的低延迟NOMA-V2X网络资源分配

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484529

Huiyi Ding, Ka-Cheong Leung

{"title":"Resource Allocation for Low-Latency NOMA-V2X Networks Using Reinforcement Learning","authors":"Huiyi Ding, Ka-Cheong Leung","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484529","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484529","url":null,"abstract":"With the development of the Internet of things (IoT), vehicle-to-everything (V2X) plays an essential role in wireless communication networks. Vehicular communications meet tremendous challenges in guaranteeing low-latency transmission for safety-critical information due to dynamic channels caused by high mobility. To handle the challenges, non-orthogonal multiple access (NOMA) has been considered as a promising candidate for future V2X networks. However, it is still an open issue on how to organize multiple transmission links with suitable resource allocation. In this paper, we investigate the problem of the resource allocation for the low-latency NOMA-integrated V2X (NOMA-V2X) communication networks. First, a cross-layer optimization problem is formulated to consider user scheduling and power allocation jointly while satisfying the quality-of-service (QoS) requirements, including the delay requirements, rate demands, and power constraints. To cope with the limited time-varying channel information, a machine learning based resource allocation algorithm is proposed to find solutions. Specifically, reinforcement learning is applied to learn the dynamic channel information for reducing the transmission delay. The numerical results indicate that our proposed algorithm can significantly reduce the system delay compared with other methods while satisfying the QoS requirements, so as to tackle the congestion issues for V2X communications.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"404 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122732533","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: 7

Frequency-aware Trajectory and Power Control for Multi-UAV Systems 多无人机系统的频率感知轨迹与功率控制

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484552

Jason Ma, M. Ostertag, Dinesh Bharadia, T. Simunic

引用次数: 0

Joint Trajectory and Power Optimization for Energy Efficient UAV Communication Using Deep Reinforcement Learning 基于深度强化学习的高能效无人机通信联合轨迹与功率优化

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484490

Yuling Cui, Danhao Deng, Chaowei Wang, Weidong Wang

引用次数: 5

A Federated Machine Learning Protocol for Fog Networks 雾网络的联邦机器学习协议

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484485

F. Foukalas, A. Tziouvaras

引用次数: 1