2020 International Conference on Omni-layer Intelligent Systems (COINS)最新文献_第5页

COLAW: Cooperative Location Proof Architecture for VANETs based on Witnessing COLAW:基于目击的vanet协同位置证明架构

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2020-08-01 DOI: 10.1109/COINS49042.2020.9191402

Philippos Barabas, Emanuel Regnath, S. Steinhorst

{"title":"COLAW: Cooperative Location Proof Architecture for VANETs based on Witnessing","authors":"Philippos Barabas, Emanuel Regnath, S. Steinhorst","doi":"10.1109/COINS49042.2020.9191402","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191402","url":null,"abstract":"Vehicular applications heavily rely on location information to improve road safety and efficiency as well as to provide a personalized driving experience through a variety of location-based services. To determine their position, vehicles depend on different technologies like GPS, which might be unreliable or vulnerable to interference or spoofing. In the safety-critical vehicular world, a secure mechanism must be in place which guarantees the accuracy and trustworthiness of location information to the service that requires it. In this work we propose COLAW, a COoperative Location proof Architecture based on Witnessing that leverages the distributed nature of vehicular ad-hoc networks to create verifiable and secure location proofs. The evaluation of COLAW shows that it is possible for a group of neighboring vehicles to generate secure location proofs for each other with a significantly lower message overhead than previously proposed approaches and that the protocol’s performance can be further improved, by taking certain environmental parameters and road conditions into consideration.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997851","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

Distributed Ledger and Smart Contract Based Approach for IoT Sensor Applications 基于分布式账本和智能合约的物联网传感器应用方法

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2020-08-01 DOI: 10.1109/COINS49042.2020.9191409

Christoph Lehnert, Grischan Engel, Thomas Greiner

引用次数: 2

Towards Safer Roads: A Deep Learning-Based Multimodal Fatigue Monitoring System 迈向更安全的道路:基于深度学习的多模态疲劳监测系统

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2020-08-01 DOI: 10.1109/COINS49042.2020.9191418

M. Hashemi, Bahareh J. Farahani, F. Firouzi

引用次数: 4

Automatic Identification of Wireless Sensor Network Topology in a IoT Domestic Setup and Discovery of User Routines 物联网家庭设置中无线传感器网络拓扑的自动识别和用户例程的发现

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2020-08-01 DOI: 10.1109/COINS49042.2020.9191423

Joao Falcao, Paulo Menezes, R. Rocha

{"title":"Automatic Identification of Wireless Sensor Network Topology in a IoT Domestic Setup and Discovery of User Routines","authors":"Joao Falcao, Paulo Menezes, R. Rocha","doi":"10.1109/COINS49042.2020.9191423","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191423","url":null,"abstract":"In recent years, Internet of Things has been gaining popularity due to its capabilities and flexible implementation. Current developments make use of several sensor types building large wireless sensor networks, where each sensor can have a degree of connection over the others. It is usually more perceptible with the use of motion sensors in different rooms where physical paths taken by a subject are strongly correlated to temporal sequences detected in the nodes. This study presents two methods for the detection of these correlations between nodes, one requiring the user to perform a path across every sensor and another method that tries to infer information without any explicit human intervention, by analysing the first events of each day where entropy is low. The results show that the latter method, which does not require explicit human intervention, presents some degradation if a low number of sensors is used in the network and these sensors have a high periodic activation. The former method is in general more accurate for small to medium sized networks, but can be problematic in large networks where passing across every sensor can be a tedious or unpractical requirement.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128804073","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

optimizing Transmission of IoT Nodes in Dynamic Environments 优化物联网节点在动态环境中的传输

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2020-08-01 DOI: 10.1109/COINS49042.2020.9191674

Gilles Callebaut, Geoffrey Ottoy, L. Perre

引用次数: 2

AlphaNet: An Attention Guided Deep Network for Automatic Image Matting AlphaNet:一种用于自动图像抠图的注意力引导深度网络

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2020-03-07 DOI: 10.1109/COINS49042.2020.9191371

Rishab Sharma, Rahul Deora, Anirudha Vishvakarma

{"title":"AlphaNet: An Attention Guided Deep Network for Automatic Image Matting","authors":"Rishab Sharma, Rahul Deora, Anirudha Vishvakarma","doi":"10.1109/COINS49042.2020.9191371","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191371","url":null,"abstract":"In this paper, we propose an end to end solution for image matting i.e high-precision extraction of foreground objects from natural images. Image matting and background detection can be achieved easily through chroma keying in a studio setting when the background is either pure green or blue. Nonetheless, image matting in natural scenes with complex and uneven depth backgrounds remains a tedious task that requires human intervention. To achieve complete automatic foreground extraction in natural scenes, we propose a method that assimilates semantic segmentation and deep image matting processes into a single network to generate detailed semantic mattes for image composition task. The contribution of our proposed method is two-fold, firstly it can be interpreted as a fully automated semantic image matting method and secondly as a refinement of existing semantic segmentation models.We propose a novel model architecture as a combination of segmentation and matting that unifies the function of upsampling and downsampling operators with the notion of attention. As shown in our work, attention guided downsampling and upsampling can extract high-quality boundary details, unlike other normal downsampling and upsampling techniques. For achieving the same, we utilized an attention guided encoder-decoder framework which does unsupervised learning for generating an attention map adaptively from the data to serve and direct the upsampling and downsampling operators. We also construct a fashion e-commerce focused dataset with high-quality alpha mattes to facilitate the training and evaluation for image matting.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699388","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

CHaPR: Efficient Inference of CNNs via Channel Pruning 第三章:基于信道剪枝的cnn高效推理

2020 International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2019-08-08 DOI: 10.1109/COINS49042.2020.9191636

Boyu Zhang, A. Davoodi, Y. Hu

引用次数: 5